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    <abstract>In this paper, we presented our team ”IIITRanchi” for the Trolling, Aggression and Cyberbullying (TRAC-3) 2022 shared tasks. Aggression and its different forms on social media and other platforms had tremendous growth on the Internet. In this work we have tried upon different aspects of aggression, aggression intensity, bias of different forms and their usage online and its identification using different Machine Learning techniques. We have classified each sample at seven different tasks namely aggression level, aggression intensity, discursive role, gender bias, religious bias, caste/class bias and ethnicity/racial bias as specified in the shared tasks. Both of our teams tried machine learning classifiers and achieved the good results. Overall, our team ”IIITRanchi” ranked first position in this shared tasks competition.</abstract>
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%0 Conference Proceedings
%T Bias, Threat and Aggression Identification Using Machine Learning Techniques on Multilingual Comments
%A Kumari, Kirti
%A Srivastav, Shaury
%A Suman, Rajiv Ranjan
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%Y Kadar, Daniel
%S Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F kumari-etal-2022-bias
%X In this paper, we presented our team ”IIITRanchi” for the Trolling, Aggression and Cyberbullying (TRAC-3) 2022 shared tasks. Aggression and its different forms on social media and other platforms had tremendous growth on the Internet. In this work we have tried upon different aspects of aggression, aggression intensity, bias of different forms and their usage online and its identification using different Machine Learning techniques. We have classified each sample at seven different tasks namely aggression level, aggression intensity, discursive role, gender bias, religious bias, caste/class bias and ethnicity/racial bias as specified in the shared tasks. Both of our teams tried machine learning classifiers and achieved the good results. Overall, our team ”IIITRanchi” ranked first position in this shared tasks competition.
%U https://aclanthology.org/2022.trac-1.4
%P 30-36

Markdown (Informal)

[Bias, Threat and Aggression Identification Using Machine Learning Techniques on Multilingual Comments](https://aclanthology.org/2022.trac-1.4) (Kumari et al., TRAC 2022)

• Bias, Threat and Aggression Identification Using Machine Learning Techniques on Multilingual Comments (Kumari et al., TRAC 2022)

ACL

• Kirti Kumari, Shaury Srivastav, and Rajiv Ranjan Suman. 2022. Bias, Threat and Aggression Identification Using Machine Learning Techniques on Multilingual Comments. In Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022), pages 30–36, Gyeongju, Republic of Korea. Association for Computational Linguistics.