index.json

[{"authors":["hate-alert"],"categories":null,"content":"Hate alert is a group of researchers at CNeRG Lab, IIT Kharagpur, India. Our vision is to bring civility in online conversations by building systems to analyse, detect and mitigate hate in online social media. We have published papers in top conferences like NeurIPS, LREC, AAAI, IJCAI, WWW, ECML-PKDD, CSCW, ICWSM, HyperText and WebSci.\nSome of the important links for researchers\n List of our papers is linked here. Check our Huggingface organisation - hate-alert Our hatexplain dataset can be found here  Notion page containing hate speech papers.  ","date":1679905800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":1679905800,"objectID":"21c347bc933409ea74d4405ee1332599","permalink":"/authors/hate-alert/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/hate-alert/","section":"authors","summary":"Hate alert is a group of researchers at CNeRG Lab, IIT Kharagpur, India. Our vision is to bring civility in online conversations by building systems to analyse, detect and mitigate hate in online social media. We have published papers in top conferences like NeurIPS, LREC, AAAI, IJCAI, WWW, ECML-PKDD, CSCW, ICWSM, HyperText and WebSci.\nSome of the important links for researchers\n List of our papers is linked here. Check our Huggingface organisation - hate-alert Our hatexplain dataset can be found here  Notion page containing hate speech papers.","tags":null,"title":"Hate Alert","type":"authors"},{"authors":["Animesh-Mukherjee"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"83aceb5349c522a776eed128de6f3436","permalink":"/authors/animesh-mukherjee/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/animesh-mukherjee/","section":"authors","summary":"","tags":null,"title":"Animesh Mukherjee","type":"authors"},{"authors":["binny"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"035a875831d945720f2e33b3ccd64bea","permalink":"/authors/binny/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/binny/","section":"authors","summary":"","tags":null,"title":"Binny Mathew","type":"authors"},{"authors":["Chris-Biemann"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"e2fb868b4ebd240ba0b6916d74b0e8c1","permalink":"/authors/chris-biemann/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/chris-biemann/","section":"authors","summary":"","tags":null,"title":"Chris Biemann","type":"authors"},{"authors":["Kiran-Garimella"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"8030fca85e5fc316fc6645c97b5e57f9","permalink":"/authors/kiran-garimella/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/kiran-garimella/","section":"authors","summary":"","tags":null,"title":"Kiran Garimella","type":"authors"},{"authors":["mithun"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"f0af5dc606ff9e19969e3517c5085ef1","permalink":"/authors/mithun/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/mithun/","section":"authors","summary":"","tags":null,"title":"Mithun Das","type":"authors"},{"authors":["pawang"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"e2190989c80245079245580e755b24f0","permalink":"/authors/pawang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/pawang/","section":"authors","summary":"","tags":null,"title":"Pawan Goyal","type":"authors"},{"authors":["punyajoy"],"categories":null,"content":"Punyajoy Saha is a PhD student at CNeRG Lab in the Department of Computer Science and Engineering at IIT Kharagpur, West Bengal. Currently, he is doing research in the area of social computing and natural language processing domain under the supervision of Prof. Animesh Mukherjee. Apart from research, he also loves playing guitar and writing short stories.\n","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"284a63f6da7faf8af3f017258dfedc0c","permalink":"/authors/punyajoy/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/punyajoy/","section":"authors","summary":"Punyajoy Saha is a PhD student at CNeRG Lab in the Department of Computer Science and Engineering at IIT Kharagpur, West Bengal. Currently, he is doing research in the area of social computing and natural language processing domain under the supervision of Prof. Animesh Mukherjee. Apart from research, he also loves playing guitar and writing short stories.","tags":null,"title":"Punyajoy Saha","type":"authors"},{"authors":["somnath"],"categories":null,"content":"Somnath Banerjee is a PhD student at CNeRG Lab in the Department of Computer Science and Engineering at IIT Kharagpur, West Bengal. Currently, he is doing research in the area of natural language processing domain under the supervision of Prof. Animesh Mukherjee.\n","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"cae360486f364854c268a46fe43c987c","permalink":"/authors/somnath/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/somnath/","section":"authors","summary":"Somnath Banerjee is a PhD student at CNeRG Lab in the Department of Computer Science and Engineering at IIT Kharagpur, West Bengal. Currently, he is doing research in the area of natural language processing domain under the supervision of Prof. Animesh Mukherjee.","tags":null,"title":"Somnath Banerjee","type":"authors"},{"authors":["Vikram-Gupta"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"64f961e74bb96b76f56513d21bba9871","permalink":"/authors/vikram-gupta/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/vikram-gupta/","section":"authors","summary":"","tags":null,"title":"Vikram Gupta","type":"authors"},{"authors":null,"categories":null,"content":"Flexibility This feature can be used for publishing content such as:\n Online courses Project or software documentation Tutorials  The courses folder may be renamed. For example, we can rename it to docs for software/project documentation or tutorials for creating an online course.\nDelete tutorials To remove these pages, delete the courses folder and see below to delete the associated menu link.\nUpdate site menu After renaming or deleting the courses folder, you may wish to update any [[main]] menu links to it by editing your menu configuration at config/_default/menus.toml.\nFor example, if you delete this folder, you can remove the following from your menu configuration:\n[[main]] name = \u0026#34;Courses\u0026#34; url = \u0026#34;courses/\u0026#34; weight = 50 Or, if you are creating a software documentation site, you can rename the courses folder to docs and update the associated Courses menu configuration to:\n[[main]] name = \u0026#34;Docs\u0026#34; url = \u0026#34;docs/\u0026#34; weight = 50 Update the docs menu If you use the docs layout, note that the name of the menu in the front matter should be in the form [menu.X] where X is the folder name. Hence, if you rename the courses/example/ folder, you should also rename the menu definitions in the front matter of files within courses/example/ from [menu.example] to [menu.\u0026lt;NewFolderName\u0026gt;].\n","date":1536451200,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1536451200,"objectID":"59c3ce8e202293146a8a934d37a4070b","permalink":"/courses/example/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/courses/example/","section":"courses","summary":"Learn how to use Academic's docs layout for publishing online courses, software documentation, and tutorials.","tags":null,"title":"Overview","type":"docs"},{"authors":null,"categories":null,"content":"In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\nTip 2 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"74533bae41439377bd30f645c4677a27","permalink":"/courses/example/example1/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/courses/example/example1/","section":"courses","summary":"In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim.","tags":null,"title":"Example Page 1","type":"docs"},{"authors":null,"categories":null,"content":"Here are some more tips for getting started with Academic:\nTip 3 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\nTip 4 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"1c2b5a11257c768c90d5050637d77d6a","permalink":"/courses/example/example2/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/courses/example/example2/","section":"courses","summary":"Here are some more tips for getting started with Academic:\nTip 3 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus.","tags":null,"title":"Example Page 2","type":"docs"},{"authors":["[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","Divyanshu Sheth","Kushal Kedia","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":" Link to the supplementary file\nMain contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1689379200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689379200,"objectID":"625c22ea2fbcf8348d93e7f781d2b333","permalink":"/publication/saha2023ecai/","publishdate":"2023-07-15T00:00:00Z","relpermalink":"/publication/saha2023ecai/","section":"publication","summary":"Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, models trained using these datasets do not perform well in cross-domain evaluation settings. To overcome this, a common strategy is to use a few samples from the target domain to train models to get better performance in that domain (cross-domain few-shot training). However, this might cause the models to overfit the artefacts of those samples. A compelling solution could be to guide the models toward rationales, i.e., spans of text that justify the text’s label. This method has been found to improve model performance in the in-domain setting across various NLP tasks. In this paper, we propose RGFS (Rationale-Guided Few-Shot Classification) for abusive language detection. We first build a multitask learning setup to jointly learn rationales, targets, and labels, and find a significant improvement of 6% macro F1 on the rationale detection task over training solely rationale classifiers. We introduce two rationale-integrated BERT-based architectures (the RGFS models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RGFS-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains. Furthermore, RGFS-based models outperform LIME/SHAP-based approaches in terms of plausibility and are close in performance in terms of faithfulness","tags":["Our papers","counterspeech","English","Generation"],"title":"Rationale-Guided Few-Shot Classification to Detect Abusive Language","type":"publication"},{"authors":["[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Kiran Garimella](https://scholar.google.co.in/citations?user=PH96F4oAAAAJ\u0026hl=en\u0026oi=ao)","Narla Komal Kalyana","Saurabh Kumar Pandeya","Pauras Mangesh Meher","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Significance Existential fear has always been a concern across human history and even transcends to the rest of the animal world. This fear is so deeply ingrained that even the slightest “knock” to it could spark a violent conflict among different groups. Here, we demonstrate how social media platforms are used to extensively mediate elements of existential fear as fear speech posts. Their nontoxic and argumentative nature makes them appealing to even benign users who in turn contribute to their wide prevalence by resharing, liking, and replying to them. Remarkably, this prevalence is far stronger than the more well-known hate speech posts. Our work necessitates consolidated moderation efforts and awareness campaigns to mitigate the harmful effects of fear speech.\nMain contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1685750400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1685750400,"objectID":"b7f46d9fcea133052ec8982e2e09b902","permalink":"/publication/saha2022pnas/","publishdate":"2023-06-03T00:00:00Z","relpermalink":"/publication/saha2022pnas/","section":"publication","summary":"Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name suggests, attempts to incite fear about a target community. Although subtle, it might be highly effective, often pushing communities toward a physical conflict. Therefore, understanding their prevalence in social media is of paramount importance. This article presents a large-scale study to understand the prevalence of 400K fear speech and over 700K hate speech posts collected from Gab.com. Remarkably, users posting a large number of fear speech accrue more followers and occupy more central positions in social networks than users posting a large number of hate speech. They can also reach out to benign users more effectively than hate speech users through replies, reposts, and mentions. This connects to the fact that, unlike hate speech, fear speech has almost zero toxic content, making it look plausible. Moreover, while fear speech topics mostly portray a community as a perpetrator using a (fake) chain of argumentation, hate speech topics hurl direct multitarget insults, thus pointing to why general users could be more gullible to fear speech. Our findings transcend even to other platforms (Twitter and Facebook) and thus necessitate using sophisticated moderation policies and mass awareness to combat fear speech..","tags":["Our papers","counterspeech","English","Generation"],"title":"On the rise of fear speech in online social media","type":"publication"},{"authors":["[Mithun Das](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mithun+Das\u0026btnG=)","Rohit Raj","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Manish Gupta](https://scholar.google.com/citations?user=eX9PSu0AAAAJ\u0026hl=en)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1685664000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1685664000,"objectID":"30989a402e050fc95d886f37cfcdd648","permalink":"/publication/das2023hatemm/","publishdate":"2023-06-02T00:00:00Z","relpermalink":"/publication/das2023hatemm/","section":"publication","summary":"Hate speech has become one of the most significant issues inmodern society, having implications in both the online and theoffline world. Due to this, hate speech research has recentlygained a lot of traction. However, most of the work has pri-marily focused on text media with relatively little work on im-ages and even lesser on videos. Thus, early stage automatedvideo moderation techniques are needed to handle the videosthat are being uploaded to keep the platform safe and healthy.With a view to detect and remove hateful content from thevideo sharing platforms, our work focuses on hate video de-tection using multi-modalities. To this end, we curate∼43hours of videos from BitChute and manually annotate themas hate or non-hate, along with the frame spans which couldexplain the labelling decision. To collect the relevant videoswe  harnessed  search  keywords  from  hate  lexicons.  We  ob-serve various cues in images and audio of hateful videos. Fur-ther, we build deep learning multi-modal models to classifythe hate videos and observe that using all the modalities ofthe videos improves the overall hate speech detection perfor-mance (accuracy=0.798, macro F1-score=0.790) by∼5.7%compared to the best uni-modal model in terms of macro F1score. In summary, our work takes the first step toward under-standing and modeling hateful videos on video hosting plat-forms such as BitChute.","tags":["Our Papers","Detection","Abusive language","Multi-Modal"],"title":"HateMM: A Multi-Modal Dataset for Hate Video Classification","type":"publication"},{"authors":["Hate Alert"],"categories":null,"content":"Important updates  Slides can be found here Video of the tutorial can be found here!!  Contributions and achievements  Our papers are accepted in top conferences/journals like PNAS, NeurIPS, LREC, AAAI, IJCAI, WWW, ECML-PKDD, CSCW, ICWSM, HyperText and WebSci. Link to the papers here We have open sourced our codes and datasets under a single github organisation - hate-alert for the future research in this domain We have stored different transformers models in huggingface.co. Link to hatealert organisation Dataset from our recent accepted paper in AAAI - \u0026ldquo;Hatexplain:A Benchmark Dataset for Explainable Hate Speech Detection\u0026rdquo; is also stored in the huggingface datsets forum We also participate in several hate speech shared tasks, winning many of them - hatealert@URDU_SOC, hatealert@DLTEACL, hateminers@AMI, hatemonitors@HASOC and coming under 1% in hatealert@Hatememe detection by Facebook AI.  Notion page containing hate speech papers.  Tutorial Outline Outline of the Tutorial\n Introduction (25 mins) Analysis (40 mins)  Prevalence of hate speech. Targets of hate speech. Effects of hate speech. Effect of offline events.   Detection (40 mins)  Summary of different datasets.  Unimodal. Multimodal.   Earlier detection models. Current detection models . Multimodal and multilingual hate speech. Challenge.  Evaluation. Explainability. Bias.     Mitigation (40 mins).  Counterspeech campaigns. Banning and suspending users. Counterspeech detection. Counterspeech generation. Effect of counter speech.   Demo (15 mins). Future Challenge (10 mins).  About the Organizers Punyajoy Saha is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lies in the nexus of social computing and natural language processing. More about him can be found here.\nBinny Mathew is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in computational social science and natural language processing. More about him can be found here.\nMithun Das is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lie in computational social science and natural language processing. More about him can be found here.\nAnimesh Mukherjee is an Associate Professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in natural language processing, information retrieval and AI and ethics. More about him can be found here.\n","date":1679905800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1679905800,"objectID":"fbf42f5ff83c66033310fd856df00eb9","permalink":"/talk/wsdm_tutorial/","publishdate":"2023-01-25T08:15:38+05:30","relpermalink":"/talk/wsdm_tutorial/","section":"talk","summary":"In this translation style tutorial presented at WSDM 2023, we present an exposition of hate speech detection and mitigation and also lay down future path for hate speech research","tags":[],"title":"Hate speech: Detection, Mitigation and Beyond @WSDM","type":"talk"},{"authors":["[Vikram Gupta](https://scholar.google.com/citations?user=jNjvdEgAAAAJ\u0026hl=en\u0026oi=ao)","Sumegh Roychowdhury","[Mithun Das](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mithun+Das\u0026btnG=)","[Somnath Banerjee](https://scholar.google.co.in/citations?user=X5Zh5BwAAAAJ\u0026hl=en)","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","Hastagiri Prakash Vanchinathan","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1664928000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664928000,"objectID":"f2b101f183c367362bbbc1b038170e5d","permalink":"/publication/vikram2022macd/","publishdate":"2022-10-05T00:00:00Z","relpermalink":"/publication/vikram2022macd/","section":"publication","summary":"Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging challenge for automated detection where code-mixing or more than one language have been used for conversation in social media. Typically, hate speech detection models are evaluated by measuring their performance on the held-out test data  using metrics such as accuracy and F1-score. While these metrics are useful, it becomes difficult to identify using them where the model is failing, and how to resolve it. To enable more targeted diagnostic insights of such multilingual hate speech models, we introduce a set of functionalities for the purpose of evaluation. We have been inspired to design this kind of functionalities based on real-world conversation on social media. Considering Hindi as a base language, we craft test cases for each functionality. We name our evaluation dataset HateCheckHIn. To illustrate the utility of these functionalities , we test state-of-the-art transformer based m-BERT model and the Perspective API.","tags":["Our Papers","Detection","Abusive language","Indic","Multilingual"],"title":"Multilingual Abusive Comment Detection at Scale for Indic Languages","type":"publication"},{"authors":["[Millon Madhur Das](https://scholar.google.co.in/citations?hl=en\u0026user=5J4ADbcAAAAJ)","[Punyajoy Saha](https://scholar.google.co.in/citations?hl=en\u0026user=VGBwCtsAAAAJ)","[Mithun Das](https://scholar.google.co.in/citations?hl=en\u0026user=tebayusAAAAJ)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1656288000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1656288000,"objectID":"31047b094689dcdd30c19a4bae893931","permalink":"/publication/das2022toxic/","publishdate":"2022-06-27T00:00:00Z","relpermalink":"/publication/das2022toxic/","section":"publication","summary":"The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxicity label is often tricky. Hence, few of the past works transform the same task into a regression. This paper shows the comparative evaluation of different transformers and traditional machine learning models on a recently released toxicity severity measurement dataset by Jigsaw. We further demonstrate the issues with the model predictions using explainability analysis.","tags":["Our Papers","Toxic speech","Toxicity rating"],"title":"Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments","type":"publication"},{"authors":["[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","Kanishk Singh","Adarsh Kumar","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":" Link to the supplementary file\nMain contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1651276800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1651276800,"objectID":"d76d35c92157938b728e5685d90e7d21","permalink":"/publication/saha2022countergedi/","publishdate":"2022-04-30T00:00:00Z","relpermalink":"/publication/saha2022countergedi/","section":"publication","summary":"Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online hate. However, since these suggestions are from a vanilla generation model, they might not include the appropriate properties required to counter a particular hate speech instance.  In this paper, we propose CounterGeDi - an ensemble of generative discriminators (GeDi) to guide the generation of a DialoGPT model toward more polite, detoxified, and emotionally laden counterspeech. We generate counterspeech using three datasets and observe significant improvement across different attribute scores. The politeness and detoxification scores increased by around 15% and 6% respectively, while the emotion in the counterspeech increased by at least 10% across all the datasets. We also experiment with triple-attribute control and observe significant improvement over single attribute results when combining complementing attributes, e.g., politeness, joyfulness and detoxification. In all these experiments, the relevancy of the generated text does not deteriorate due to the application of these controls.","tags":["Our papers","counterspeech","English","Generation"],"title":"CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech","type":"publication"},{"authors":["[Mithun Das](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mithun+Das\u0026btnG=)","[Somnath Banerjee](https://scholar.google.co.in/citations?user=X5Zh5BwAAAAJ\u0026hl=en)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1650931200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1650931200,"objectID":"92c0a05d431c92fb23c74676da6d20ab","permalink":"/publication/das2022data/","publishdate":"2022-04-26T00:00:00Z","relpermalink":"/publication/das2022data/","section":"publication","summary":"Abusive language is a growing concern in many social media platforms. Repeated exposure to abusive speech has created physiological effects on the target users. Thus, the problem of abusive language should be addressed in all forms for online peace and safety. While extensive research exists in abusive speech detection, most studies focus on English. Recently, many smearing incidents have occurred in India, which provoked diverse forms of abusive speech in online space in various languages based on the geographic location. Therefore it is essential to deal with such malicious content. In this paper, to bridge the gap, we demonstrate a large-scale analysis of multilingual abusive speech in Indic languages. We examine different interlingual transfer mechanisms and observe the performance of various multilingual models for abusive speech detection for eight different Indic languages. We also experiment to show how robust these models are on adversarial attacks. Finally, we conduct an in-depth error analysis by looking into the models' misclassified posts across various settings. We have made our code and models public for other researchers.","tags":["Our Papers","Detection","Abusive language","Multilingual"],"title":"Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages","type":"publication"},{"authors":["[Mithun Das](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mithun+Das\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1650931200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1650931200,"objectID":"ac692277c50f7f494bb61296fe27bfb9","permalink":"/publication/das2022hatecheckin/","publishdate":"2022-04-26T00:00:00Z","relpermalink":"/publication/das2022hatecheckin/","section":"publication","summary":"Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging challenge for automated detection where code-mixing or more than one language have been used for conversation in social media. Typically, hate speech detection models are evaluated by measuring their performance on the held-out test data  using metrics such as accuracy and F1-score. While these metrics are useful, it becomes difficult to identify using them where the model is failing, and how to resolve it. To enable more targeted diagnostic insights of such multilingual hate speech models, we introduce a set of functionalities for the purpose of evaluation. We have been inspired to design this kind of functionalities based on real-world conversation on social media. Considering Hindi as a base language, we craft test cases for each functionality. We name our evaluation dataset HateCheckHIn. To illustrate the utility of these functionalities , we test state-of-the-art transformer based m-BERT model and the Perspective API.","tags":["Our Papers","Detection","Abusive language","Hindi"],"title":"HateCheckHIn: Evaluating Hindi Hate Speech Detection Models","type":"publication"},{"authors":["Hate Alert"],"categories":null,"content":"Important updates  Slides can be found here  Contributions and achievements  Our papers are accepted in top conferences like AAAI, WWW, CSCW, ICWSM, WebSci. Link to the papers here We have open sourced our codes and datasets under a single github organisation - hate-alert for the future research in this domain We have stored different transformers models in huggingface.co. Link to hatealert organisation Dataset from our recent accepted paper in AAAI - \u0026ldquo;Hatexplain:A Benchmark Dataset for Explainable Hate Speech Detection\u0026rdquo; is also stored in the huggingface datsets forum We also participate in several hate speech shared tasks, winning many of them - hatealert@URDU_SOC, hatealert@DLTEACL, hateminers@AMI, hatemonitors@HASOC and coming under 1% in hatealert@Hatememe detection by Facebook AI.  Notion page containing hate speech papers.  Tutorial Outline In this translation style tutorial, we present an exposition of hate speech detection and mitigation in three steps. The following section presents a detailed plan for the tutorial:-\n Introduction (15 min)- This section will cover the scentific interest in hate speech and various definitions of hate speech. This section will help you understand the outline and what to take home from this tutorial. Analysis (20 min)- In this section, we analyze the spread of hate speech in online social media platforms like Twitter, Facebook, Gab etc. We observe that hate speech is spreading through online communities at an alarming rate. These hateful users are well connected among themselves and are reaching a wider audience. This case is more severe in moderation free platforms like Gab, Bitchute etc. The targets of such hate vary. These include the Muslims, Jews, Africans etc. This section is further divided into the following parts  Spread of hate speech Effects of hate speech Targets of hate speech   Detection (20 min)- Hate speech detection is a challenging task. We now have several datasets available based on different criterias language, domain, modalities etc.Several models ranging from simple Bag of Words to complex ones like BERT have been used for the task. The task performance seems to be improving over time, however, there are issues like generalizability, bias and explainability of the models. This section is further divided into  Different datasets. Earlier detection models Current detection models (based on transformers) Multimodal and Multilingual hate speech Hate user detection Challenge: Evaluation, Explainability and Bias   Mitigation (20 min)- To deter the spread of hate speech, organizations have adopted several policies. These include the general policies like deletion of posts and/or accounts, shadow banning to softer approaches like counterspeech. Policies like banning/deletion seem to be effective in some cases, but there are issues of violation of freedom of speech. Recent research have started looking into automated generation of counterspeech as well.  Banning and suspending users Counter speech detection Counter speech generation Challenges: Generation pitfalls, Moderation effects   Road to the future (15 min)- We end this tutorial with covering the summary of the challenges and road to the future for hate speech research.  Summary of challenges Branches and extensions of hate speech. Connections to offline violence. Guidelines for building better dataset. Adapting to newer events and platforms.    About the Organizers Punyajoy Saha is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lies in the nexus of social computing and natural language processing. More about him can be found here.\nBinny Mathew is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in computational social science and natural language processing. More about him can be found here.\nMithun Das is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lie in computational social science and natural language processing. More about him can be found here.\nAnimesh Mukherjee is an Associate Professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in natural language processing, information retrieval and AI and ethics. More about him can be found here.\n","date":1645633800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1645633800,"objectID":"4f165e148f3686d5c9fbeae7ac9fe80d","permalink":"/talk/aaai_tutorial/","publishdate":"2021-03-02T08:15:38+05:30","relpermalink":"/talk/aaai_tutorial/","section":"talk","summary":"In this translation style tutorial presented at AAAI 2022, we present an exposition of hate speech detection and mitigation and also lay down future path for hate speech research","tags":[],"title":"Hate speech: Detection, Mitigation and Beyond @AAAI","type":"talk"},{"authors":["[Mithun Das](https://scholar.google.com/citations?hl=en\u0026view_op=search_authors\u0026mauthors=mithun+das\u0026btnG=)","[Somnath Banerjee](https://scholar.google.com/citations?hl=en\u0026view_op=search_authors\u0026mauthors=Somnath+Banerjee\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?hl=en\u0026view_op=search_authors\u0026mauthors=punyajoy+saha\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1637971200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1637971200,"objectID":"018e265dc6d3e7fead1478dcd8e1fa9b","permalink":"/publication/banerjee2021urdu/","publishdate":"2021-11-27T00:00:00Z","relpermalink":"/publication/banerjee2021urdu/","section":"publication","summary":"Online hatred is a growing concern on many social media platforms. To address this issue, different social media platforms have introduced moderation policies for such content. They also employ moderators who can check the posts violating moderation policies and take appropriate action. Academicians in the abusive language research domain also perform various studies to detect such content better. Although there is extensive research in abusive language detection in English, there is a lacuna in abusive language detection in low resource languages like Hindi, Urdu etc. In this FIRE 2021 shared task - HASOC- Abusive and Threatening language detection in Urdu the organizers propose an abusive language detection dataset in Urdu along with threatening language detection. In this paper, we explored several machine learning models such as XGboost, LGBM, m-BERT based models for abusive and threatening content detection in Urdu based on the shared task. We observed the Transformer model specifically trained on abusive language dataset in Arabic helps in getting the best performance. Our model came First for both abusive and threatening content detection with an F1scoreof 0.88 and 0.54, respectively.","tags":["Our papers","Urdu"],"title":"Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach","type":"publication"},{"authors":["[Somnath Banerjee](https://scholar.google.com/citations?hl=en\u0026view_op=search_authors\u0026mauthors=Somnath+Banerjee\u0026btnG=)","[Maulindu Sarkar](https://scholar.google.com/citations?hl=en\u0026view_op=search_authors\u0026mauthors=Maulindu+Sarkar\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?hl=en\u0026view_op=search_authors\u0026mauthors=punyajoy+saha\u0026btnG=)","[Mithun Das](https://scholar.google.com/citations?hl=en\u0026view_op=search_authors\u0026mauthors=mithun+das\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1637971200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1637971200,"objectID":"90a96098fabf42796a59c98d7e20168a","permalink":"/publication/banerjee2021exploring/","publishdate":"2021-11-27T00:00:00Z","relpermalink":"/publication/banerjee2021exploring/","section":"publication","summary":"Hate speech is considered to be one of the major issues currently plaguing online social media. Repeated and repetitive exposure to hate speech has been shown to create physiological effects on the target users. Thus, hate speech, in all its forms, should be addressed on these platforms in order to maintain good health. In this paper, we explored several Transformer based machine learning models for the detection of hate speech and offensive content in English and Indo-Aryan languages at FIRE 2021. We explore several models such as mBERT, XLMR-large, XLMR-base by team name Super Mario. Our models came 2nd position in Code-Mixed Data set (Macro F1: 0.7107), 2nd position in Hindi two-class classification(Macro F1: 0.7797), 4th in English four-class category (Macro F1: 0.8006) and 12th in English two-class category (Macro F1: 0.6447)","tags":["Our papers","Indo-Aryan","classification"],"title":"Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan Languages","type":"publication"},{"authors":["[Mithun Das](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mithun+Das\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Ritam Dutt](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ritam+Dutt\u0026btnG=)","[Pawan Goyal](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pawan+Goyal\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1627603200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1627603200,"objectID":"38ca51f78759e95510d07c55b75dbcdc","permalink":"/publication/das2021hatefuluser/","publishdate":"2021-07-30T00:00:00Z","relpermalink":"/publication/das2021hatefuluser/","section":"publication","summary":"Hate speech is regarded as one of the crucial issues plaguing the online social media. The current literature on hate speech detection leverages primarily the textual content to find hateful posts and subsequently identify hateful users. However, this methodology disregards the social connections between users. In this paper, we run a detailed exploration of the problem space and investigate an array of models ranging from purely textual to graph based to finally semi-supervised techniques using Graph Neural Networks (GNN) that utilize both textual and graph-based features. We run exhaustive experiments on two datasets -- Gab, which is loosely moderated and Twitter, which is strictly moderated. Overall the AGNN model achieves 0.791 macro F1-score on the Gab dataset and 0.780 macro F1-score on the Twitter dataset using only 5% of the labeled instances, considerably outperforming all the other models including the fully supervised ones. We perform detailed error analysis on the best performing text and graph based models and observe that hateful users have unique network neighborhood signatures and the AGNN model benefits by paying attention to these signatures. This property, as we observe, also allows the model to generalize well across platforms in a zero-shot setting. Lastly, we utilize the best performing GNN model to analyze the evolution of hateful users and their targets over time in Gab. ","tags":["Our Papers","Detection","Hateful users","Hate speech"],"title":"You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions \u0026 Insights","type":"publication"},{"authors":["Hate Alert"],"categories":null,"content":"Important updates  Slides can be found here Video of the tutorial can be found here!!  Contributions and achievements  Our papers are accepted in top conferences like AAAI, WWW, CSCW, ICWSM, WebSci. Link to the papers here We have open sourced our codes and datasets under a single github organisation - hate-alert for the future research in this domain We have stored different transformers models in huggingface.co. Link to hatealert organisation Dataset from our recent accepted paper in AAAI - \u0026ldquo;Hatexplain:A Benchmark Dataset for Explainable Hate Speech Detection\u0026rdquo; is also stored in the huggingface datsets forum We also participate in several hate speech shared tasks, winning many of them - hatealert@URDU_SOC, hatealert@DLTEACL, hateminers@AMI, hatemonitors@HASOC and coming under 1% in hatealert@Hatememe detection by Facebook AI.  Notion page containing hate speech papers.  Tutorial Outline In this translation style tutorial, we present an exposition of hate speech detection and mitigation in three steps. The following section presents a detailed plan for the tutorial:-\n Introduction (15 min)- This section will cover the scentific interest in hate speech and various definitions of hate speech. This section will help you understand the outline and what to take home from this tutorial. Analysis (20 min)- In this section, we analyze the spread of hate speech in online social media platforms like Twitter, Facebook, Gab etc. We observe that hate speech is spreading through online communities at an alarming rate. These hateful users are well connected among themselves and are reaching a wider audience. This case is more severe in moderation free platforms like Gab, Bitchute etc. The targets of such hate vary. These include the Muslims, Jews, Africans etc. This section is further divided into the following parts  Spread of hate speech Effects of hate speech Targets of hate speech   Detection (20 min)- Hate speech detection is a challenging task. We now have several datasets available based on different criterias language, domain, modalities etc.Several models ranging from simple Bag of Words to complex ones like BERT have been used for the task. The task performance seems to be improving over time, however, there are issues like generalizability, bias and explainability of the models.  Different datasets. This section is further divided into Earlier detection models Current detection models (based on transformers) Multimodal and Multilingual hate speech Hate user detection Challenge: Evaluation, Explainability and Bias   Mitigation (20 min)- To deter the spread of hate speech, organizations have adopted several policies. These include the general policies like deletion of posts and/or accounts, shadow banning to softer approaches like counterspeech. Policies like banning/deletion seem to be effective in some cases, but there are issues of violation of freedom of speech. Recent research have started looking into automated generation of counterspeech as well.  Banning and suspending users Counter speech detection Counter speech generation Challenges: Generation pitfalls, Moderation effects   Road to the future (15 min)- We end this tutorial with covering the summary of the challenges and road to the future for hate speech research.  Summary of challenges Branches and extensions of hate speech. Connections to offline violence. Guidelines for building better dataset. Adapting to newer events and platforms.    About the Organizers Punyajoy Saha is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lies in the nexus of social computing and natural language processing. More about him can be found here.\nBinny Mathew is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in computational social science and natural language processing. More about him can be found here.\nMithun Das is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lie in computational social science and natural language processing. More about him can be found here.\nPawan Goyal is an Associate Professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in natural language processing and text mining. More about him can be found here.\nKiran Garimella is the first IDSS postdoctoral fellow to receive a Hammer Fellowship, pioneers research into the spread of rumors and misinformation on closed platforms such as WhatsApp, a popular encrypted messaging service with millions of users worldwide. More about him can be found here.\nAnimesh Mukherjee is an Associate Professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in natural language processing, information retrieval and AI and ethics. More about him can be found here.\n","date":1623070800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1623070800,"objectID":"9eb46877140873b0b88d4801198a69bb","permalink":"/talk/icwsm_tutorial/","publishdate":"2021-03-02T08:15:38+05:30","relpermalink":"/talk/icwsm_tutorial/","section":"talk","summary":"In this translation style tutorial presented at ICWSM 2021, we present an exposition of hate speech detection and mitigation and also lay down future path for hate speech research","tags":[],"title":"Hate speech: Detection, Mitigation and Beyond @ICWSM","type":"talk"},{"authors":["Hate Alert"],"categories":[],"content":"We analyze the spread of hate speech in online social media platforms like Twitter, Facebook, Gab etc. We observe that hate speech is spreading through online communities at an alarming rate. These hateful users are well connected among themselves and are reaching a wider audience. This case is more severe in moderation free platforms like Gab, Bitchute etc. The targets of such hate vary. These include the Muslims, Jews, Africans etc.\n","date":1614711544,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1614711544,"objectID":"50068b7e213536ddfa1624237247c86b","permalink":"/project/analysis/","publishdate":"2021-03-03T00:29:04+05:30","relpermalink":"/project/analysis/","section":"project","summary":"We analyze the spread of hate speech in online social media platforms like Twitter, Facebook, Gab etc. We observe that hate speech is spreading through online communities at an alarming rate. These hateful users are well connected among themselves and are reaching a wider audience. This case is more severe in moderation free platforms like Gab, Bitchute etc. The targets of such hate vary. These include the Muslims, Jews, Africans etc.","tags":[],"title":"Analysis","type":"project"},{"authors":["[Debjoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Debjoy+Saha\u0026btnG=)","[Naman Paharia](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Naman+Paharia\u0026btnG=)","[Debajit Chakraborty](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Debajit+Chakraborty\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1613692800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1613692800,"objectID":"9939ef4731714a86608f14dd1e904ef9","permalink":"/publication/saha2021hate/","publishdate":"2021-02-19T00:00:00Z","relpermalink":"/publication/saha2021hate/","section":"publication","summary":"Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task, Offensive Language Identification in Dravidian Languages at EACL 2021, we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.","tags":["Offensive","Our papers","Tamil","Detection","Kannada"],"title":"Hate-Alert@ DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection","type":"publication"},{"authors":["[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Kiran Garimella](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Kiran+Garimella\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1612828800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1612828800,"objectID":"6c0645826fa58b0629c519fd5d3752db","permalink":"/publication/saha2021short/","publishdate":"2021-02-09T00:00:00Z","relpermalink":"/publication/saha2021short/","section":"publication","summary":"WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it was used to connect to the voters on a large scale. Along with the campaigning, there have been reports that WhatsApp has also become a breeding ground for harmful speech against various protected groups and religious minorities. Many such messages attempt to instil fear among the population about a specific (minority) community. According to research on inter-group conflict, such \"fear speech\" messages could have a lasting impact and might lead to real offline violence. In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We curate a new dataset and try to characterize fear speech from this dataset. We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task. Fear speech messages tend to spread faster and could potentially go undetected by classifiers built to detect traditional toxic speech due to their low toxic nature. Finally, using a novel methodology to target users with Facebook ads, we conduct a survey among the users of these WhatsApp groups to understand the types of users who consume and share fear speech. We believe that this work opens up new research questions that are very different from tackling hate speech which the research community has been traditionally involved in.","tags":["Our papers","Detection","Hindi","English","Fear speech"],"title":"Short is the Road that Leads from Fear to Hate: Fear Speech in Indian WhatsApp Groups","type":"publication"},{"authors":["[Poojitha Maheshappa](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Poojitha+Maheshappa\u0026btnG=)","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1609459200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609459200,"objectID":"87c08ad66d9d55ee946fb0fed67f218f","permalink":"/publication/maheshappa2021using/","publishdate":"2021-01-01T00:00:00Z","relpermalink":"/publication/maheshappa2021using/","section":"publication","summary":" With the increasing cases of online hate speech, there is an urgentdemand for better hate speech detection systems. In this paper, weutilize Knowledge Graphs (KGs) to improve hate speech detection.Our initial results shows that incorporating information from KGhelps the classifier to improve the performance. ","tags":["English","Our papers","Detection","Knowledge graph"],"title":"Using Knowledge Graphs to Improve Hate Speech Detection","type":"publication"},{"authors":["[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Seid Muhie Yimam](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Seid+Muhie+Yimam\u0026btnG=)","[Chris Biemann](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Chris+Biemann\u0026btnG=)","[Pawan Goyal](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pawan+Goyal\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1608249600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1608249600,"objectID":"5b46fd1c171efe2626134fbd430a695b","permalink":"/publication/mathew2020hatexplain/","publishdate":"2020-12-18T00:00:00Z","relpermalink":"/publication/mathew2020hatexplain/","section":"publication","summary":"Hate speech is a challenging issue plaguing the online social media. While better models for hate speech detection are continuously being developed, there is little research on the bias and interpretability aspects of hate speech. In this paper, we introduce HateXplain, the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in our dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based. We utilize existing state-of-the-art models and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness. We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities. We have made our code and dataset public","tags":["English","Our papers","Dataset","Detection","Explainability"],"title":"HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection","type":"publication"},{"authors":["[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Anurag Illendula](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Anurag+Illendula\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Soumya Sarkar](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Soumya+Sarkar\u0026btnG=)","[Pawan Goyal](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pawan+Goyal\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1595635200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1595635200,"objectID":"1e20f33e696fdbe5b7ed12e16f1d0abb","permalink":"/publication/mathew2019temporal/","publishdate":"2020-07-25T00:00:00Z","relpermalink":"/publication/mathew2019temporal/","section":"publication","summary":"With the ongoing debate on ‘freedom of speech’ vs. ‘hate speech,’ there is an urgent need to carefullyunderstand the consequences of the inevitable culmination of the two, i.e., ‘freedom of hate speech’ over time.An ideal scenario to understand this would be to observe the effects of hate speech in an (almost) unrestricted environment. Hence, we perform the first temporal analysis of hate speech on Gab.com, a social media site with very loose moderation policy. We first generatetemporal snapshotsof Gab from millions of posts and users. Using these temporal snapshots, we compute anactivity vectorbased on DeGroot model to identify hateful users. The amount of hate speech in Gab is steadily increasing and the new users are becoming hatefulat an increased and faster rate. Further, our analysis analysis reveals that the hate users are occupying the prominent positions in the Gab network. Also, the language used by the community as a whole seem tocorrelate more with that of the hateful users as compared to the non-hateful ones. We discuss how, many crucial design questions in CSCW open up from our work.","tags":["Our papers","English"],"title":"Hate begets Hate: A Temporal Study of Hate Speech","type":"publication"},{"authors":["Hate Alert"],"categories":[],"content":"Hate speech detection is a challenging task. We now have several datasets available based on different criterias language, domain, modalities etc.Several models ranging from simple Bag of Words to complex ones like BERT have been used for the task. The task performance seems to be improving over time, however, there are issues like generalizability, bias and explainability of the models.\n","date":1583175396,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1583175396,"objectID":"477813931710146f41d6177009f4258a","permalink":"/project/detection/","publishdate":"2020-03-03T00:26:36+05:30","relpermalink":"/project/detection/","section":"project","summary":"Hate speech detection is a challenging task. We now have several datasets available based on different criterias language, domain, modalities etc.Several models ranging from simple Bag of Words to complex ones like BERT have been used for the task. The task performance seems to be improving over time, however, there are issues like generalizability, bias and explainability of the models.","tags":[],"title":"Detection","type":"project"},{"authors":["[Son T Luu](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Son+T+Luu\u0026btnG=)","[Hung P Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Hung+P+Nguyen\u0026btnG=)","[Kiet Van Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Kiet+Van+Nguyen\u0026btnG=)","[Ngan Luu-Thuy Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ngan+Luu-Thuy+Nguyen\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1577836800,"objectID":"cf80adffa031f7762e0270861ab0e0c1","permalink":"/publication/luu2020comparison/","publishdate":"2020-01-01T00:00:00Z","relpermalink":"/publication/luu2020comparison/","section":"publication","summary":"Hate-speech detection on social network language has become one of the main researching fields recently due to the spreading of social networks like Facebook and Twitter. In Vietnam, the threat of offensive and harassment cause bad impacts for online user. The VLSP-Shared task about Hate Speech Detection on social networks showed many proposed approaches for detecting whatever comment is clean or not. However, this problem still needs further researching. Consequently, we compare traditional machine","tags":["English","Detection","Vietnamese"],"title":"Comparison Between Traditional Machine Learning Models And Neural Network Models For Vietnamese Hate Speech Detection","type":"publication"},{"authors":["[Sai Saketh Aluru](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Sai+Saketh+Aluru\u0026btnG=)","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1577836800,"objectID":"4c638041466b040b3deb8fabe916321f","permalink":"/publication/saketh2020deep/","publishdate":"2020-01-01T00:00:00Z","relpermalink":"/publication/saketh2020deep/","section":"publication","summary":"Hate speech detection is a challenging problem with most of the datasets available in only one language: English. In this paper, we conduct a large scale analysis of multilingual hate speech in 9 languages from 16 different sources. We observe that in low resource setting, simple models such as LASER embedding with logistic regression performs the best, while in high resource setting BERT based models perform better. In case of zero-shot classification, languages such as Italian and Portuguese achieve good results. Our proposed framework could be used as an efficient solution for low-resource languages. These models could also act as good baselines for future multilingual hate speech detection tasks.","tags":["Our papers","Detection"],"title":"Deep Learning Models for Multilingual Hate Speech Detection","type":"publication"},{"authors":["[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Navish Kumar](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Navish+Kumar\u0026btnG=)","[Pawan Goyal](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pawan+Goyal\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1577836800,"objectID":"8b33034c1173ce7f06bfff95e2a98616","permalink":"/publication/mathew2020interaction/","publishdate":"2020-01-01T00:00:00Z","relpermalink":"/publication/mathew2020interaction/","section":"publication","summary":"Social media platforms usually tackle the proliferation of hate speech by blocking/suspending the message or account. One of the major drawback of such measures is the restriction of free speech. In this paper, we investigate the interaction of hatespeech and the responses that counter it (aka counter-speech). One of the prime contribution of this work is that we developed and released1 a dataset where we annotate pairs of hate and counter users.","tags":["Counter speech","English","Our papers"],"title":"Interaction dynamics between hate and counter users on Twitter","type":"publication"},{"authors":["admin"],"categories":null,"content":"Supplementary notes can be added here, including code and math.\n","date":1554595200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1554595200,"objectID":"557dc08fd4b672a0c08e0a8cf0c9ff7d","permalink":"/publication/preprint/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/preprint/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example preprint / working paper","type":"publication"},{"authors":["Hate Alert"],"categories":[],"content":"To deter the spread of hate speech, organizations have adopted several policies. These include the general policies like deletion of posts and/or accounts, shadow banning to softer approaches like counterspeech. Policies like banning/deletion seem to be effective in some cases, but there are issues of violation of freedom of speech. Recent research have started looking into automated generation of counterspeech as well.\n","date":1551553150,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1551553150,"objectID":"33d51623f8acc0ca8960142a9f603fa8","permalink":"/project/mitigation/","publishdate":"2019-03-03T00:29:10+05:30","relpermalink":"/project/mitigation/","section":"project","summary":"To deter the spread of hate speech, organizations have adopted several policies. These include the general policies like deletion of posts and/or accounts, shadow banning to softer approaches like counterspeech. Policies like banning/deletion seem to be effective in some cases, but there are issues of violation of freedom of speech. Recent research have started looking into automated generation of counterspeech as well.","tags":null,"title":"Mitigation","type":"project"},{"authors":[],"categories":[],"content":"Welcome to Slides  Academic\n Features  Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides   Controls  Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click  PDF Export: E   Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026#34;blueberry\u0026#34; if porridge == \u0026#34;blueberry\u0026#34;: print(\u0026#34;Eating...\u0026#34;)  Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\n Fragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne Two Three  A fragment can accept two optional parameters:\n class: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears   Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\n Only the speaker can read these notes Press S key to view    Themes  black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links    night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links   Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026#34;/img/boards.jpg\u0026#34; \u0026gt;}} {{\u0026lt; slide background-color=\u0026#34;#0000FF\u0026#34; \u0026gt;}} {{\u0026lt; slide class=\u0026#34;my-style\u0026#34; \u0026gt;}}  Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; }  Questions?  Ask\n Documentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Academic's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":["[Jing Qian](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Jing+Qian\u0026btnG=)","[Anna Bethke](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Anna+Bethke\u0026btnG=)","[Yinyin Liu](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Yinyin+Liu\u0026btnG=)","[Elizabeth Belding](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Elizabeth+Belding\u0026btnG=)","[William Yang Wang](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=William+Yang+Wang\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"d7726fa0f6da25a0a2420394ec8ad04c","permalink":"/publication/qian2019benchmark/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/qian2019benchmark/","section":"publication","summary":"Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future. In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context. In this","tags":["English","Dataset"],"title":"A benchmark dataset for learning to intervene in online hate speech","type":"publication"},{"authors":["[Arijit Ghosh Chowdhury](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Arijit+Ghosh+Chowdhury\u0026btnG=)","[Aniket Didolkar](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Aniket+Didolkar\u0026btnG=)","[Ramit Sawhney](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ramit+Sawhney\u0026btnG=)","[Rajiv Shah](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Rajiv+Shah\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"79c1325bd0ddc192e26c8ee60c4cdc82","permalink":"/publication/chowdhury2019arhnet/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/chowdhury2019arhnet/","section":"publication","summary":"The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language. Religious Hate Speech, in particular, often leads to unrest and sometimes aggravates to violence against people on the basis of their religious affiliations. The richness of the Arabic morphology and the limited available resources makes this task especially challenging. The current state-of-the-art approaches to detect hate speech in Arabic rely entirely on textual (lexical and semantic)","tags":["Arabic","Detection"],"title":"ARHNet-Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic","type":"publication"},{"authors":["[Yi-Ling Chung](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Yi-Ling+Chung\u0026btnG=)","[Elizaveta Kuzmenko](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Elizaveta+Kuzmenko\u0026btnG=)","[Serra Sinem Tekiroglu](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Serra+Sinem+Tekiroglu\u0026btnG=)","[Marco Guerini](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Marco+Guerini\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"cd5ff0a714b9e5c1d5af42f476eb2f76","permalink":"/publication/chung2019conan/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/chung2019conan/","section":"publication","summary":"Although there is an unprecedented effort to provide adequate responses in terms of laws and policies to hate content on social media platforms, dealing with hatred online is still a tough problem. Tackling hate speech in the standard way of content deletion or user suspension may be charged with censorship and overblocking. One alternate strategy, that has received little attention so far by the research community, is to actually oppose hate content with counter-narratives (ie informed textual responses). In this paper, we describe","tags":["Dataset"],"title":"CONAN--COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech","type":"publication"},{"authors":["[Areej Al-Hassan](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Areej+Al-Hassan\u0026btnG=)","[Hmood Al-Dossari](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Hmood+Al-Dossari\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"98f298e1965ec4c05088a843305e3c35","permalink":"/publication/al2019detection/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/al2019detection/","section":"publication","summary":"In social media platforms, hate speech can be a reason of “cyber conflict” which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an","tags":["Survey","Detection"],"title":"Detection of hate speech in social networks: a survey on multilingual corpus","type":"publication"},{"authors":["[Katharine Gelber](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Katharine+Gelber\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"611ed678fdfe135a2a01e57878ad19c5","permalink":"/publication/gelber2019differentiating/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/gelber2019differentiating/","section":"publication","summary":"In this paper I develop a systemic discrimination approach to defining a narrowly construed category of hate speech, as speech that harms to a sufficient degree to warrant government regulation. This is important due to the lack of definitional clarity, and the extraordinarily wide","tags":["English"],"title":"Differentiating hate speech: a systemic discrimination approach","type":"publication"},{"authors":["[Tin Van Huynh](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Tin+Van+Huynh\u0026btnG=)","[Vu Duc Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Vu+Duc+Nguyen\u0026btnG=)","[Kiet Van Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Kiet+Van+Nguyen\u0026btnG=)","[Ngan Luu-Thuy Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ngan+Luu-Thuy+Nguyen\u0026btnG=)","[Anh Gia-Tuan Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Anh+Gia-Tuan+Nguyen\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"79a60e55512f11ec54581557c896727c","permalink":"/publication/van2019hate/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/van2019hate/","section":"publication","summary":"In recent years, Hate Speech Detection has become one of the interesting fields in natural language processing or computational linguistics. In this paper, we present the description of our system to solve this problem at the VLSP shared task 2019: Hate Speech Detection on","tags":["English","Detection","Vietnamese"],"title":"Hate Speech Detection on Vietnamese Social Media Text using the Bi-GRU-LSTM-CNN Model","type":"publication"},{"authors":["[Ziqi Zhang](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ziqi+Zhang\u0026btnG=)","[Lei Luo](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Lei+Luo\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"4d89371a7f2bf801fd01e12b029655ac","permalink":"/publication/zhang2019hate/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/zhang2019hate/","section":"publication","summary":"In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and researchers. A large number of methods have been developed for","tags":["English"],"title":"Hate speech detection: A solved problem? the challenging case of long tail on twitter","type":"publication"},{"authors":["[Natalie Alkiviadou](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Natalie+Alkiviadou\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"64fcf2066c93b3c2342eefb5fbe741ae","permalink":"/publication/alkiviadou2019hate/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/alkiviadou2019hate/","section":"publication","summary":"Social networks serve as effective platforms in which users ideas can be spread in an easy and efficient manner. However, those ideas can be hateful and harmful, some of which may even amount to hate speech. YouTube, Facebook and Twitter have internal regulatory","tags":["English"],"title":"Hate speech on social media networks: towards a regulatory framework?","type":"publication"},{"authors":["[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Pawan Goyal](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pawan+Goyal\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"51c216323bed6b835bf0e8de8a03ead0","permalink":"/publication/saha2019hatemonitors/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/saha2019hatemonitors/","section":"publication","summary":"Reducing hateful and offensive content in online social media  pose  a  dual  problem  for  the  moderators.  On  the  one  hand,  rigid censorship  on  social  media  cannot  be  imposed.  On  the  other,  the free flow of such content cannot be allowed. Hence, we require efficient abusive language detection system to detect such harmful content in socialmedia. In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019.We have used Gradient Boosting model, along with BERT and LASER embeddings,  to  make  the  system  language  agnostic.  Our  model  came at First position for the German sub-task A.","tags":["Our papers","Detection","English"],"title":"HateMonitors: Language Agnostic Abuse Detection in Social Media","type":"publication"},{"authors":["[Jing Qian](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Jing+Qian\u0026btnG=)","[Mai ElSherief](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mai+ElSherief\u0026btnG=)","[Elizabeth Belding](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Elizabeth+Belding\u0026btnG=)","[William Yang Wang](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=William+Yang+Wang\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"3534b5115a15d9d43b03302a5c4122d6","permalink":"/publication/qian2019learning/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/qian2019learning/","section":"publication","summary":"Existing computational models to understand hate speech typically frame the problem as a simple classification task, bypassing the understanding of hate symbols (e.g., 14 words, kigy) and their secret connotations. In this paper, we propose a novel task of deciphering hate symbols. To do this, we leverage the Urban Dictionary and collected a new, symbol-rich Twitter corpus of hate speech. We investigate neural network latent context models for deciphering hate symbols. More specifically, we study Sequence-to-Sequence models and show how they are able to crack the ciphers based on context. Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.","tags":["English","Dataset"],"title":"Learning to Decipher Hate Symbols","type":"publication"},{"authors":["[Thomas Mandl](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Thomas+Mandl\u0026btnG=)","[Sandip Modha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Sandip+Modha\u0026btnG=)","[Prasenjit Majumder](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Prasenjit+Majumder\u0026btnG=)","[Daksh Patel](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Daksh+Patel\u0026btnG=)","[Mohana Dave](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mohana+Dave\u0026btnG=)","[Chintak Mandlia](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Chintak+Mandlia\u0026btnG=)","[Aditya Patel](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Aditya+Patel\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"9d3bb6703d7815b0fba238276f915e3d","permalink":"/publication/mandl2019overview/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/mandl2019overview/","section":"publication","summary":"The identification of Hate Speech in Social Media is of great importance and receives much attention in the text classification community. There is a huge demand for research for languages other than English. The HASOC track intends to stimulate","tags":["Offensive","English"],"title":"Overview of the hasoc track at fire 2019: Hate speech and offensive content identification in indo-european languages","type":"publication"},{"authors":["[Sylvia Jaki](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Sylvia+Jaki\u0026btnG=)","[Tom De Smedt](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Tom+De+Smedt\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"6f577f75b9dce5d2d6c7f0e5348c1b33","permalink":"/publication/jaki2019right/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/jaki2019right/","section":"publication","summary":"Discussion about the social network Twitter often concerns its role in political discourse, involving the question of when an expression of opinion becomes offensive, immoral, and/or illegal, and how to deal with it. Given the growing amount of offensive communication on the internet, there is a demand for new technology that can automatically detect hate speech, to assist content moderation by humans. This comes with new challenges, such as defining exactly what is free speech and what is illegal in a specific country, and knowing exactly","tags":["Detection","German"],"title":"Right-wing German hate speech on Twitter: Analysis and automatic detection","type":"publication"},{"authors":["[Valerio Basile](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Valerio+Basile\u0026btnG=)","[Cristina Bosco](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Cristina+Bosco\u0026btnG=)","[Elisabetta Fersini](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Elisabetta+Fersini\u0026btnG=)","[Debora Nozza](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Debora+Nozza\u0026btnG=)","[Viviana Patti](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Viviana+Patti\u0026btnG=)","[Francisco Manuel Rangel Pardo](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Francisco+Manuel+Rangel+Pardo\u0026btnG=)","[Paolo Rosso](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Paolo+Rosso\u0026btnG=)","[Manuela Sanguinetti](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Manuela+Sanguinetti\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"71d56378267412967897bf9d11df7e2f","permalink":"/publication/basile2019semeval/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/basile2019semeval/","section":"publication","summary":"The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary","tags":["Detection"],"title":"Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter","type":"publication"},{"authors":["[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Ritam Dutt](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ritam+Dutt\u0026btnG=)","[Pawan Goyal](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pawan+Goyal\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"693f270fc3bdf2ee02dac6fad800f338","permalink":"/publication/mathew2019spread/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/mathew2019spread/","section":"publication","summary":"The present online social media platform is afflicted with several issues, with hate speech being on the predominant forefront. The prevalence of online hate speech has fueled horrific real-world hate-crime such as the mass-genocide of Rohingya Muslims, communal violence in Colombo and the recent massacre in the Pittsburgh synagogue. Consequently, It is imperative to understand the diffusion of such hateful content in an online setting. We conduct the first study that analyses the flow and dynamics of posts generated by hateful and non-hateful users on Gab (gab.com) over a massive dataset of 341K users and 21M posts. Our observations confirms that hateful content diffuse farther, wider and faster and have a greater outreach than those of non-hateful users. A deeper inspection into the profiles and network of hateful and non-hateful users reveals that the former are more influential, popular and cohesive. Thus, our research explores the interesting facets of diffusion dynamics of hateful users and broadens our understanding of hate speech in the online world.","tags":["English","Our papers"],"title":"Spread of hate speech in online social media","type":"publication"},{"authors":["[Maarten Sap](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Maarten+Sap\u0026btnG=)","[Dallas Card](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Dallas+Card\u0026btnG=)","[Saadia Gabriel](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Saadia+Gabriel\u0026btnG=)","[Yejin Choi](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Yejin+Choi\u0026btnG=)","[Noah A Smith](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Noah+A+Smith\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"b2fb5858a601378e4c471682b3f03e24","permalink":"/publication/sap2019risk/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/sap2019risk/","section":"publication","summary":"We investigate how annotators insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. We first uncover unexpected correlations between surface markers of African","tags":["Bias","Racial","Detection","English"],"title":"The risk of racial bias in hate speech detection","type":"publication"},{"authors":["[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Hardik Tharad](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Hardik+Tharad\u0026btnG=)","[Subham Rajgaria](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Subham+Rajgaria\u0026btnG=)","[Prajwal Singhania](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Prajwal+Singhania\u0026btnG=)","[Suman Kalyan Maity](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Suman+Kalyan+Maity\u0026btnG=)","[Pawan Goyal](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pawan+Goyal\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"600772c1c3a47273d5549ed8abd74de6","permalink":"/publication/mathew2019thou/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/mathew2019thou/","section":"publication","summary":"Hate content in social media is ever increasing. While Facebook, Twitter, Google have attempted to take several steps to tackle the hateful content, they have mostly been unsuccessful. Counterspeech is seen as an effective way of tackling the online hate without any harm to the freedom of speech. Thus, an alternative strategy for these platforms could be to promote counterspeech as a defense against hate content. However, in order to have a successful promotion of such counterspeech, one has to have a deep understanding of its dynamics in the online world. Lack of carefully curated data largely inhibits such understanding. In this paper, we create and release the first ever dataset for counterspeech using comments from YouTube. The data contains 13,924 manually annotated comments where the labels indicate whether a comment is a counterspeech or not. This data allows us to perform a rigorous measurement study characterizing the linguistic structure of counterspeech for the first time. This analysis results in various interesting insights such as: the counterspeech comments receive much more likes as compared to the noncounterspeech comments, for certain communities majority of the non-counterspeech comments tend to be hate speech, the different types of counterspeech are not all equally effective and the language choice of users posting counterspeech is largely different from those posting non-counterspeech as revealed by a detailed psycholinguistic analysis. Finally, we build a set of machine learning models that are able to automatically detect counterspeech in YouTube videos with an F1-score of 0.71. We also build multilabel models that can detect different types of counterspeech in a comment with an F1-score of 0.60.","tags":["Counter speech","English","Our Papers"],"title":"Thou shalt not hate: Countering online hate speech","type":"publication"},{"authors":["[Thai Binh Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Thai+Binh+Nguyen\u0026btnG=)","[Quang Minh Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Quang+Minh+Nguyen\u0026btnG=)","[Thu Hien Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Thu+Hien+Nguyen\u0026btnG=)","[Ngoc Phuong Pham](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ngoc+Phuong+Pham\u0026btnG=)","[The Loc Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=The+Loc+Nguyen\u0026btnG=)","[Quoc Truong Do](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Quoc+Truong+Do\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"9ef959e23a3042650bbb0e9bad7bfc24","permalink":"/publication/nguyen2019vais/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/nguyen2019vais/","section":"publication","summary":"Nowadays, Social network sites (SNSs) such as Facebook, Twitter are common places where people show their opinions, sentiments and share information with others. However, some people use SNSs to post abuse and harassment threats in order to prevent other SNSs users from expressing themselves as well as seeking different opinions. To deal with this problem, SNSs have to use a lot of resources including people to clean the aforementioned content. In this paper, we propose a supervised learning model based on","tags":["English","Detection","Vietnamese"],"title":"VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination","type":"publication"},{"authors":["[Wafa Alorainy](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Wafa+Alorainy\u0026btnG=)","[Pete Burnap](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pete+Burnap\u0026btnG=)","[Han Liu](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Han+Liu\u0026btnG=)","[Matthew L Williams](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Matthew+L+Williams\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"52806f950131a209aa7225aa986fec7f","permalink":"/publication/alorainy2019enemy/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/alorainy2019enemy/","section":"publication","summary":"Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyberhate) has been frequently posted and widely circulated via the World Wide Web. This can be considered as","tags":["English"],"title":"“The Enemy Among Us” Detecting Cyber Hate Speech with Threats-based Othering Language Embeddings","type":"publication"},{"authors":["[Paula Fortuna](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Paula+Fortuna\u0026btnG=)","[Sergio Nunes](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Sergio+Nunes\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"fdc192fe562baf975ebbf068828b1486","permalink":"/publication/fortuna2018survey/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/fortuna2018survey/","section":"publication","summary":"The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This","tags":["Survey","Detection","English"],"title":"A survey on automatic detection of hate speech in text","type":"publication"},{"authors":["[Manuela Sanguinetti](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Manuela+Sanguinetti\u0026btnG=)","[Fabio Poletto](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Fabio+Poletto\u0026btnG=)","[Cristina Bosco](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Cristina+Bosco\u0026btnG=)","[Viviana Patti](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Viviana+Patti\u0026btnG=)","[Marco Stranisci](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Marco+Stranisci\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"f5e298b2218d8c0c52abe28876cd12c1","permalink":"/publication/sanguinetti2018italian/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/sanguinetti2018italian/","section":"publication","summary":"The paper describes a recently-created Twitter corpus of about 6,000 tweets, annotated for hate speech against immigrants, and developed to be a reference dataset for an automatic system of hate speech monitoring. The annotation scheme was therefore specifically designed to account for the multiplicity of factors that can contribute to the definition of a hate speech notion, and to offer a broader tagset capable of better representing all those factors, which may increase, or rather mitigate, the impact of the message. This resulted in a scheme","tags":["Dataset"],"title":"An italian twitter corpus of hate speech against immigrants","type":"publication"},{"authors":["[Nuha Albadi](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Nuha+Albadi\u0026btnG=)","[Maram Kurdi](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Maram+Kurdi\u0026btnG=)","[Shivakant Mishra](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Shivakant+Mishra\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"3967fd0271689c1d593112d8d4b42d15","permalink":"/publication/albadi2018they/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/albadi2018they/","section":"publication","summary":"Religious hate speech in the Arabic Twittersphere is a notable problem that requires developing automated tools to detect messages that use inflammatory sectarian language to promote hatred and violence against people on the basis of religious affiliation. Distinguishing hate speech from other profane and vulgar language is quite a challenging task that requires deep linguistic analysis. The richness of the Arabic morphology and the limited available resources for the Arabic language make this task even more challenging","tags":["Arabic","Detection"],"title":"Are they our brothers? Analysis and detection of religious hate speech in the Arabic Twittersphere","type":"publication"},{"authors":["[Ziqi Zhang](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ziqi+Zhang\u0026btnG=)","[David Robinson](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=David+Robinson\u0026btnG=)","[Jonathan Tepper](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Jonathan+Tepper\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"e82d051510d4012bd4e8a6be6e1064dd","permalink":"/publication/zhang2018detecting/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/zhang2018detecting/","section":"publication","summary":"In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to","tags":["English"],"title":"Detecting hate speech on twitter using a convolution-gru based deep neural network","type":"publication"},{"authors":["[M Ali Fauzi](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=M+Ali+Fauzi\u0026btnG=)","[Anny Yuniarti](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Anny+Yuniarti\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"c537070374507e880b94f6a5652d8c11","permalink":"/publication/fauzi2018ensemble/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/fauzi2018ensemble/","section":"publication","summary":"Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of","tags":["Detection","Indonesian"],"title":"Ensemble method for indonesian twitter hate speech detection","type":"publication"},{"authors":["[Mai ElSherief](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mai+ElSherief\u0026btnG=)","[Vivek Kulkarni](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Vivek+Kulkarni\u0026btnG=)","[Dana Nguyen](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Dana+Nguyen\u0026btnG=)","[William Yang Wang](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=William+Yang+Wang\u0026btnG=)","[Elizabeth Belding](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Elizabeth+Belding\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"1df82419778d516b8ebf64ad467b55c8","permalink":"/publication/elsherief2018hate/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/elsherief2018hate/","section":"publication","summary":"While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech--its target: either directed towards a specific person or entity, or generalized towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech","tags":["English","Dataset"],"title":"Hate lingo: A target-based linguistic analysis of hate speech in social media","type":"publication"},{"authors":["[Ona de Gibert](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ona+de+Gibert\u0026btnG=)","[Naiara Perez](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Naiara+Perez\u0026btnG=)","[Aitor Garcia-Pablos](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Aitor+Garcia-Pablos\u0026btnG=)","[Montse Cuadros](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Montse+Cuadros\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"2ac3fe2434e7a39ae1248ad180b4c723","permalink":"/publication/de2018hate/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/de2018hate/","section":"publication","summary":"Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic. Due to the massive rise of user-generated web content on social media, the amount of hate speech is also steadily increasing. Over the past years, interest in online hate speech detection and, particularly, the automation of this task has continuously grown, along with the societal impact of the","tags":["English","Dataset"],"title":"Hate speech dataset from a white supremacy forum","type":"publication"},{"authors":["[James Weinstein](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=James+Weinstein\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"8c064b5783af36609d773b5c5dd030f4","permalink":"/publication/weinstein2018hate/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/weinstein2018hate/","section":"publication","summary":"This book, devoted to acquainting reader with the basics of American free speech doctrine, presents a description of the radical attack on modern free speech doctrine. It discusses whether banning this speech would be a remedy for the harms hate speech and","tags":["English"],"title":"Hate speech, pornography, and radical attacks on free speech doctrine","type":"publication"},{"authors":["[Punyajoy Saha](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Punyajoy+Saha\u0026btnG=)","[Binny Mathew](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Binny+Mathew\u0026btnG=)","[Pawan Goyal](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pawan+Goyal\u0026btnG=)","[Animesh Mukherjee](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Animesh+Mukherjee\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"e7b70c08223a9d8f3a99208cfc34314b","permalink":"/publication/saha2018hateminers/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/saha2018hateminers/","section":"publication","summary":"With the online proliferation of hate speech, there is an urgent need for systems that can detect such harmful content. In this paper, We present the machine learning models developed for the Automatic Misogyny Identification (AMI) shared task at EVALITA 2018. We generate three types of features: Sentence Embeddings, TF-IDF Vectors, and BOW Vectors to represent each tweet. These features are then concatenated and fed into the machine learning models. Our model came First for the English Subtask A and Fifth for the English","tags":["Our papers","English"],"title":"Hateminers: Detecting hate speech against women","type":"publication"},{"authors":["[Antigoni Maria Founta](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Antigoni+Maria+Founta\u0026btnG=)","[Constantinos Djouvas](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Constantinos+Djouvas\u0026btnG=)","[Despoina Chatzakou](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Despoina+Chatzakou\u0026btnG=)","[Ilias Leontiadis](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ilias+Leontiadis\u0026btnG=)","[Jeremy Blackburn](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Jeremy+Blackburn\u0026btnG=)","[Gianluca Stringhini](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Gianluca+Stringhini\u0026btnG=)","[Athena Vakali](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Athena+Vakali\u0026btnG=)","[Michael Sirivianos](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Michael+Sirivianos\u0026btnG=)","[Nicolas Kourtellis](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Nicolas+Kourtellis\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"5f615323310aa76c6f1e60c93a0fd315","permalink":"/publication/founta2018large/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/founta2018large/","section":"publication","summary":"In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling","tags":["English","Dataset"],"title":"Large scale crowdsourcing and characterization of twitter abusive behavior","type":"publication"},{"authors":["[Zewdie Mossie](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Zewdie+Mossie\u0026btnG=)","[Jenq-Haur Wang](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Jenq-Haur+Wang\u0026btnG=)","[Dhinaharan Nagamalai](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Dhinaharan+Nagamalai\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"a849d5f373a60850901839a0c910eaa9","permalink":"/publication/mossie2018social/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/mossie2018social/","section":"publication","summary":"The anonymity of social networks makes it attractive for hate speech to mask their criminal activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing volume of social media data, hate speech identification becomes a challenge in aggravating conflict between citizens of nations. The high rate of production, has become difficult to collect, store and analyze such big data using traditional detection methods. This paper proposed the application of apache spark in hate speech detection to","tags":["Amharic","Detection"],"title":"Social Network Hate Speech Detection for Amharic Language","type":"publication"},{"authors":["[Sarah Eissa](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Sarah+Eissa\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"e99fefb9d78f24f1409947ee6a4dcafa","permalink":"/publication/eissa2018use/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/eissa2018use/","section":"publication","summary":"This research aims to examine hate speech in the Egyptian Arabic language newspapers, through examining the front page of the three dailies, the state-owned Al-Ahram newspaper, the privately-owned newspaper Al-Masry Al-Youm and the partisan newspaper Al-Wafd. The studys time span starts from June 30, 2012 to June 30, 2015. The analysis for the study is based on the framing and agenda-setting theories in order to find how hate speech is framed and whether the speech is affected by a governmental agenda. The research uses","tags":["Arabic"],"title":"Use of hate speech in Arabic language newspapers","type":"publication"},{"authors":["[Savvas Zannettou](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Savvas+Zannettou\u0026btnG=)","[Barry Bradlyn](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Barry+Bradlyn\u0026btnG=)","[Emiliano De Cristofaro](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Emiliano+De+Cristofaro\u0026btnG=)","[Haewoon Kwak](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Haewoon+Kwak\u0026btnG=)","[Michael Sirivianos](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Michael+Sirivianos\u0026btnG=)","[Gianluca Stringini](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Gianluca+Stringini\u0026btnG=)","[Jeremy Blackburn](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Jeremy+Blackburn\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"0373e1920d8ded209af00b6743dbc719","permalink":"/publication/zannettou2018gab/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/zannettou2018gab/","section":"publication","summary":"Over the past few years, a number of new fringe communities, like 4chan or certain subreddits, have gained traction on the Web at a rapid pace. However, more often than not, little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Webs information ecosystem. In August 2016, a new social network called","tags":["English","Dataset"],"title":"What is gab: A bastion of free speech or an alt-right echo chamber","type":"publication"},{"authors":["[Mainack Mondal](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mainack+Mondal\u0026btnG=)","[Leandro Araujo Silva](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Leandro+Araujo+Silva\u0026btnG=)","[Fabricio Benevenuto](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Fabricio+Benevenuto\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"a359d73fe3656a93def23f8f9da8acf5","permalink":"/publication/mondal2017measurement/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/mondal2017measurement/","section":"publication","summary":"Social media platforms provide an inexpensive communication medium that allows anyone to quickly reach millions of users. Consequently, in these platforms anyone can publish content and anyone interested in the content can obtain it, representing a transformative revolution in our society. However, this same potential of social media systems brings together an important challenge---these systems provide space for discourses that are harmful to certain groups of people. This challenge manifests itself with a number of","tags":["English","Dataset"],"title":"A measurement study of hate speech in social media","type":"publication"},{"authors":["[Anna Schmidt](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Anna+Schmidt\u0026btnG=)","[Michael Wiegand](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Michael+Wiegand\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"664954539d3abc9fc6e5cd98948084bf","permalink":"/publication/schmidt2017survey/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/schmidt2017survey/","section":"publication","summary":"This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our","tags":["Survey","Detection","English"],"title":"A survey on hate speech detection using natural language processing","type":"publication"},{"authors":["[Thomas Davidson](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Thomas+Davidson\u0026btnG=)","[Dana Warmsley](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Dana+Warmsley\u0026btnG=)","[Michael Macy](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Michael+Macy\u0026btnG=)","[Ingmar Weber](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ingmar+Weber\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"906720c609f467a54f13cb5e4bad4be3","permalink":"/publication/davidson2017automated/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/davidson2017automated/","section":"publication","summary":"A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate","tags":["Offensive","Detection","English"],"title":"Automated hate speech detection and the problem of offensive language","type":"publication"},{"authors":["[Pinkesh Badjatiya](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Pinkesh+Badjatiya\u0026btnG=)","[Shashank Gupta](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Shashank+Gupta\u0026btnG=)","[Manish Gupta](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Manish+Gupta\u0026btnG=)","[Vasudeva Varma](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Vasudeva+Varma\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"0d9fc980ad280c929834a6271d623db0","permalink":"/publication/badjatiya2017deep/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/badjatiya2017deep/","section":"publication","summary":"Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of","tags":["Detection","English"],"title":"Deep learning for hate speech detection in tweets","type":"publication"},{"authors":["[Shervin Malmasi](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Shervin+Malmasi\u0026btnG=)","[Marcos Zampieri](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Marcos+Zampieri\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"7287f0d1f40ed4e55f9ed1e704f2248c","permalink":"/publication/malmasi2017detecting/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/malmasi2017detecting/","section":"publication","summary":"In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated","tags":["English"],"title":"Detecting hate speech in social media","type":"publication"},{"authors":["[Lei Gao](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Lei+Gao\u0026btnG=)","[Ruihong Huang](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ruihong+Huang\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"0d9a5df03e45204ef067e7db3dfe8e70","permalink":"/publication/gao2017detecting/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/gao2017detecting/","section":"publication","summary":"In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model","tags":["English","Dataset"],"title":"Detecting online hate speech using context aware models","type":"publication"},{"authors":["[Phyllis B Gerstenfeld](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Phyllis+B+Gerstenfeld\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"78bf7b6e02882fb69e4c1ada924f2f8f","permalink":"/publication/gerstenfeld2017hate/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/gerstenfeld2017hate/","section":"publication","summary":"This chapter addresses some of the main issues related to hate crime, beginning with defining them and distinguishing them from hate speech  Hate crime is sometimes also called by other names such as “bias crime” or “ethnic intimidation.” Undoubtedly, humans have","tags":["Hate crime","English"],"title":"Hate Crime","type":"publication"},{"authors":["[Madeline Masucci](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Madeline+Masucci\u0026btnG=)","[Lynn Langton](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Lynn+Langton\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"24b47ed7d49b6971c4d05bbfa90633a5","permalink":"/publication/masucci2017hate/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/masucci2017hate/","section":"publication","summary":"Lynn Langton, Ph. D., BJS Statistician In 2015, the rate of violent hate crime victimization was 0.7 hate crimes per 1,000 persons age 12 or older (figure 1). This rate was not significantly different from the rate in 2004 (0.9 per 1,000). 1 The absence of statistically","tags":["Hate crime","English"],"title":"Hate crime victimization, 2004-2015","type":"publication"},{"authors":["[Aoife O’Neill](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Aoife+O’Neill\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"7ea41b922c3a7312b4adb05b1ca002d4","permalink":"/publication/o2017hate/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/o2017hate/","section":"publication","summary":"In accordance with the Statistics and Registration Service Act 2007, statistics based on police  recorded crime data have been assessed against the Code of Practice for Official Statistics and  found not to meet the required standard for designation as National Statistics. The full assessment","tags":["English"],"title":"Hate Crime, England and Wales","type":"publication"},{"authors":["[Neil Chakraborti](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Neil+Chakraborti\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"47159127ea8e88fe18ddd9d1bf88d2e2","permalink":"/publication/chakraborti2017hate/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/chakraborti2017hate/","section":"publication","summary":"Hate crime has become an increasingly familiar term in recent times as problems of bigotry and prejudice continue to pose complex challenges for societies across the world. Although greater recognition is now afforded to hate crimes and their associated harms, the problem","tags":["English"],"title":"Hate Crime: concepts, policy, future directions","type":"publication"},{"authors":["[Fabio Del Vigna12](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Fabio+Del+Vigna12\u0026btnG=)","[Andrea Cimino23](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Andrea+Cimino23\u0026btnG=)","[Felice Dell’Orletta](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Felice+Dell’Orletta\u0026btnG=)","[Marinella Petrocchi](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Marinella+Petrocchi\u0026btnG=)","[Maurizio Tesconi](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Maurizio+Tesconi\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"8b046ffbf10ca272d899e33b4983cf13","permalink":"/publication/del2017hate/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/del2017hate/","section":"publication","summary":"While favouring communications and easing information sharing, Social Network Sites are also used to launch harmful campaigns against specific groups and individuals. Cyberbullism, incitement to self-harm practices, sexual predation are just some of the severe","tags":["Detection","English"],"title":"Hate me, hate me not: Hate speech detection on facebook","type":"publication"},{"authors":["[Fabio Poletto](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Fabio+Poletto\u0026btnG=)","[Marco Stranisci](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Marco+Stranisci\u0026btnG=)","[Manuela Sanguinetti](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Manuela+Sanguinetti\u0026btnG=)","[Viviana Patti](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Viviana+Patti\u0026btnG=)","[Cristina Bosco](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Cristina+Bosco\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"3caa7d4c99fb7066da23c5365efd927d","permalink":"/publication/poletto2017hate/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/poletto2017hate/","section":"publication","summary":"The paper describes the development of a corpus from social media built with the aim of representing and analysing hate speech against some minority groups in Italy. The issues related to data collection and annotation are introduced, focusing on the challenges we addressed in designing a multifaceted set of labels where the main features of verbal hate expressions may be modelled. Moreover, an analysis of the disagreement among the annotators is presented in order to carry out a preliminary evaluation of the data set and the","tags":["Italian"],"title":"Hate speech annotation: Analysis of an italian twitter corpus","type":"publication"},{"authors":["[Ika Alfina](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ika+Alfina\u0026btnG=)","[Rio Mulia](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Rio+Mulia\u0026btnG=)","[Mohamad Ivan Fanany](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mohamad+Ivan+Fanany\u0026btnG=)","[Yudo Ekanata](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Yudo+Ekanata\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"61969d9286a49cb9487c26fc517803aa","permalink":"/publication/alfina2017hate/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/alfina2017hate/","section":"publication","summary":"The objective of our work is to detect hate speech in the Indonesian language. As far as we know, the research on this subject is still very rare. The only research we found has created a dataset for hate speech against religion, but the quality of this dataset is inadequate. Our research aimed to create a new dataset that covers hate speech in general, including hatred for religion, race, ethnicity, and gender. In addition, we also conducted a preliminary study using machine learning approach. Machine learning so far is the most frequently used","tags":["Detection","Dataset","Indonesian"],"title":"Hate speech detection in the indonesian language: A dataset and preliminary study","type":"publication"},{"authors":["[Bjorn Ross](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Bjorn+Ross\u0026btnG=)","[Michael Rist](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Michael+Rist\u0026btnG=)","[Guillermo Carbonell](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Guillermo+Carbonell\u0026btnG=)","[Benjamin Cabrera](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Benjamin+Cabrera\u0026btnG=)","[Nils Kurowsky](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Nils+Kurowsky\u0026btnG=)","[Michael Wojatzki](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Michael+Wojatzki\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"c81312eb8349d0ef681f93d01e2a938c","permalink":"/publication/ross2017measuring/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/ross2017measuring/","section":"publication","summary":"Some users of social media are spreading racist, sexist, and otherwise hateful content. For the purpose of training a hate speech detection system, the reliability of the annotations is crucial, but there is no universally agreed-upon definition. We collected potentially hateful","tags":["English"],"title":"Measuring the reliability of hate speech annotations: The case of the european refugee crisis","type":"publication"},{"authors":["[Lei Gao](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Lei+Gao\u0026btnG=)","[Alexis Kuppersmith](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Alexis+Kuppersmith\u0026btnG=)","[Ruihong Huang](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ruihong+Huang\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"7d5b6d440f1e3522502639324ccaa42e","permalink":"/publication/gao2017recognizing/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/gao2017recognizing/","section":"publication","summary":"To address various limitations of supervised hate speech classification methods includ- ing corpus bias and huge cost of annota- tion, we propose a weakly supervised two- path bootstrapping approach for an online hate speech detection model leveraging large-scale","tags":["English"],"title":"Recognizing explicit and implicit hate speech using a weakly supervised two-path bootstrapping approach","type":"publication"},{"authors":["[Bjorn Gamback](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Bjorn+Gamback\u0026btnG=)","[Utpal Kumar Sikdar](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Utpal+Kumar+Sikdar\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"2dbd5f3e6599b182ca13261dfa9ac0bf","permalink":"/publication/gamback2017using/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/gamback2017using/","section":"publication","summary":"The paper introduces a deep learning-based Twitter hate-speech text classification system. The classifier assigns each tweet to one of four predefined categories: racism, sexism, both (racism and sexism) and non-hate-speech. Four Convolutional Neural Network models were","tags":["Detection","English"],"title":"Using convolutional neural networks to classify hate-speech","type":"publication"},{"authors":["[Imran Awan](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Imran+Awan\u0026btnG=)","[Irene Zempi](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Irene+Zempi\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"79d1d3cfc937491a36f280c9e1bb2e62","permalink":"/publication/awan2017will/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/awan2017will/","section":"publication","summary":"Anti-Muslim hate crime is usually viewed in the prism of physical attacks; however, it also occurs in a cyber context, and this reality has considerable consequences for victims. In seeking to help improve our understanding of anti-Muslim hate crime, this article draws on","tags":["Hate crime","English"],"title":"‘I will blow your face OFF’—VIRTUAL and physical world anti-muslim hate crime","type":"publication"},{"authors":["[Stephan Tulkens](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Stephan+Tulkens\u0026btnG=)","[Lisa Hilte](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Lisa+Hilte\u0026btnG=)","[Elise Lodewyckx](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Elise+Lodewyckx\u0026btnG=)","[Ben Verhoeven](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Ben+Verhoeven\u0026btnG=)","[Walter Daelemans](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Walter+Daelemans\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"49d6a08914093a54bf98146efb37c325","permalink":"/publication/tulkens2016dictionary/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/tulkens2016dictionary/","section":"publication","summary":"We present a dictionary-based approach to racism detection in Dutch social media comments, which were retrieved from two public Belgian social media sites likely to attract racist reactions. These comments were labeled as racist or non-racist by multiple annotators. For our approach, three discourse dictionaries were created: first, we created a dictionary by retrieving possibly racist and more neutral terms from the training data, and then augmenting these with more general words to remove some bias. A second dictionary was created","tags":["Dutch","Detection"],"title":"A dictionary-based approach to racism detection in dutch social media","type":"publication"},{"authors":["[Gail B Murrow](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Gail+B+Murrow\u0026btnG=)","[Richard Murrow](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Richard+Murrow\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"036ff6f6fc01100417e2214dbace1443","permalink":"/publication/murrow2016valid/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/murrow2016valid/","section":"publication","summary":"Here, we respond to three peer commentaries on our paper,A hypothetical neurological association between dehumanization and human rights abuses. 1 In that paper, we hypothesized that dehumanizing implicit biases dampen the response of neural","tags":["Bias","English"],"title":"A valid question: Could hate speech condition bias in the brain?","type":"publication"},{"authors":["[Zeerak Waseem](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Zeerak+Waseem\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"ea3d5b4dee6ed4509dda27e113534bf9","permalink":"/publication/waseem2016you/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/waseem2016you/","section":"publication","summary":"1Data set available at http://github.com/zeerakw/hatespeech 138 Page 2. 2 Data  Waseem and Hovy (2016) may suffer from personal bias, as the only the authors an- notated, and only the annotations positive for hate speech were reviewed by one other person","tags":["Detection","English"],"title":"Are you a racist or am i seeing things? annotator influence on hate speech detection on twitter","type":"publication"},{"authors":["[Mark Walters](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mark+Walters\u0026btnG=)","[Rupert Brown](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Rupert+Brown\u0026btnG=)","[Susann Wiedlitzka](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Susann+Wiedlitzka\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"39ad5cf59bdd996f0bdee7322e2b9bb9","permalink":"/publication/walters2016causes/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/walters2016causes/","section":"publication","summary":"Causes and motivations of Equality and Human Rights Commission Research report 102 Mark A. Walters and Rupert Brown with Susann Wiedlitzka, University of Sussex hate crime Page 2. Electronic copy available at: https://ssrn.com/abstract=2918883 Causes and motivations of hate","tags":["Hate crime","English"],"title":"Causes and motivations of hate crime","type":"publication"},{"authors":["[Zeerak Waseem](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Zeerak+Waseem\u0026btnG=)","[Dirk Hovy](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Dirk+Hovy\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"cce6bb7d997f8940f9d3034bbb5e3e4e","permalink":"/publication/waseem2016hateful/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/waseem2016hateful/","section":"publication","summary":"Hate speech in the form of racist and sexist remarks are a common occurrence on social media. For that reason, many social media services address the problem of identifying hate speech, but the definition of hate speech varies markedly and is largely a manual effort (BBC, 2015; Lomas, 2015). We provide a list of criteria founded in critical race theory, and use them to annotate a publicly available corpus of more than 16k tweets. We analyze the impact of various extra-linguistic features in conjunction with character n-grams for","tags":["Detection","English","Dataset"],"title":"Hateful symbols or hateful people? predictive features for hate speech detection on twitter","type":"publication"},{"authors":["[John R Blosnich](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=John+R+Blosnich\u0026btnG=)","[Mary C Marsiglio](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Mary+C+Marsiglio\u0026btnG=)","[Shasha Gao](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Shasha+Gao\u0026btnG=)","[Adam J Gordon](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Adam+J+Gordon\u0026btnG=)","[Jillian C Shipherd](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Jillian+C+Shipherd\u0026btnG=)","[Michael Kauth](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Michael+Kauth\u0026btnG=)","[George R Brown](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=George+R+Brown\u0026btnG=)","[Michael J Fine](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Michael+J+Fine\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"d8572f6ace6851714d120ee1766b95e6","permalink":"/publication/blosnich2016mental/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/blosnich2016mental/","section":"publication","summary":"Objectives. To examine whether indicators of community-and state-level lesbian, gay, bisexual, and transgender equality are associated with transgender veterans mental health. Methods. We extracted Veterans Administration data for patients who were diagnosed with","tags":["Hate crime","English"],"title":"Mental health of transgender veterans in US states with and without discrimination and hate crime legal protection","type":"publication"},{"authors":["[Imran Awan](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Imran+Awan\u0026btnG=)","[Irene Zempi](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Irene+Zempi\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"58601d1e1e0be2580089da0ad80abb7b","permalink":"/publication/awan2016affinity/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/awan2016affinity/","section":"publication","summary":"Following the recent terrorist attacks in Paris and Tunisia in 2015, and in Woolwich, south-east London where British Army soldier Drummer Lee Rigby was murdered in 2013, there has seen a significant increase in anti-Muslim attacks. These incidents have occurred offline","tags":["English"],"title":"The affinity between online and offline anti-Muslim hate crime: Dynamics and impacts","type":"publication"},{"authors":["[Jason Chan](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Jason+Chan\u0026btnG=)","[Anindya Ghose](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Anindya+Ghose\u0026btnG=)","[Robert Seamans](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Robert+Seamans\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"1bc2832ddb687f9849db070aac9f55fc","permalink":"/publication/chan2016internet/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/chan2016internet/","section":"publication","summary":"We empirically investigate the effect of the Internet on racial hate crimes in the United States from the period 2001–2008. We find evidence that, on average, broadband availability increases racial hate crimes. We also document that the Internets impact on these hate","tags":["Racial","English"],"title":"The internet and racial hate crime: Offline spillovers from online access","type":"publication"},{"authors":["[Carmen Aguilera-Carnerero](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Carmen+Aguilera-Carnerero\u0026btnG=)","[Abdul Halik Azeez](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Abdul+Halik+Azeez\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"a33fd447dd1665561b305c3db9e96f03","permalink":"/publication/aguilera2016islamonausea/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/aguilera2016islamonausea/","section":"publication","summary":"Hate speech is multifaceted: it can attribute false assumptions to a religion, ascribe despic- able facts to a religious community, mock their traditions and  22 online discourse on jihad is carried out not by Muslims themselves, but by people with a clear Islamophobic/racist bias","tags":["English"],"title":"‘Islamonausea, not Islamophobia’: The many faces of cyber hate speech","type":"publication"},{"authors":["[Wilson Jeffrey Maloba](https://scholar.google.com/citations?view_op=search_authors\u0026hl=en\u0026mauthors=Wilson+Jeffrey+Maloba\u0026btnG=)"],"categories":null,"content":"Main contributions Coming soon\nLimitations Coming soon\nFuture Directions Coming soon\n","date":1388534400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1388534400,"objectID":"df80030619b8e40fa858f3e55f3d3996","permalink":"/publication/maloba2014use/","publishdate":"2014-01-01T00:00:00Z","relpermalink":"/publication/maloba2014use/","section":"publication","summary":"Hate speech has of late become a sensitive issue in Kenya given that it helped trigger the post election violence of 2007/2008. At the same time, the percentage of the populace that has internet access has continued to grow giving rise to an active online community whose activity is scarcely monitored. The current detection of these hate messages is manual as it mostly relies on what is captured on the media or text that an online user happens to flag. Given that bloggers have come under investigation for the content they post online shows","tags":["English","Detection","Kenya"],"title":"Use of regular expressions for multi-lingual detection of hate speech in Kenya","type":"publication"}]