@inproceedings{murugan-etal-2024-challenges,
title = "Challenges and Insights in Identifying Hate Speech and Fake News on Social Media",
author = "Murugan, Shanthi and
R, Arthi and
E, Boomika and
S, Jeyanth and
S, Kaviyarasu",
editor = "Biradar, Shankar and
Reddy, Kasu Sai Kartheek and
Saumya, Sunil and
Akhtar, Md. Shad",
booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)",
month = dec,
year = "2024",
address = "AU-KBC Research Centre, Chennai, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2024.icon-fauxhate.4/",
pages = "16--21",
abstract = "Social media has transformed communication, but it has also brought abouta number of serious problems, most notablythe proliferation of hate speech and falseinformation. hate-related conversations arefrequently fueled by misleading narratives.We address this issue by building a multiclassclassification model trained on Faux HateMulti-Label Dataset (Biradar et al. 2024)which consists of hateful remarks that arefraudulent and have a code mix of Hindi andEnglish. Model has been built to classifySeverity (Low, Medium, High) and Target(Individual, Organization, Religion) on thedataset. Performance of the model isevaluated on test dataset achieved varyingscored for each. For Severity model achieves74{\%}, for Target model achieves 74{\%}. Thelimitations and performance issues of themodel has been understood and wellexplained."
}
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%0 Conference Proceedings
%T Challenges and Insights in Identifying Hate Speech and Fake News on Social Media
%A Murugan, Shanthi
%A R, Arthi
%A E, Boomika
%A S, Jeyanth
%A S, Kaviyarasu
%Y Biradar, Shankar
%Y Reddy, Kasu Sai Kartheek
%Y Saumya, Sunil
%Y Akhtar, Md. Shad
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F murugan-etal-2024-challenges
%X Social media has transformed communication, but it has also brought abouta number of serious problems, most notablythe proliferation of hate speech and falseinformation. hate-related conversations arefrequently fueled by misleading narratives.We address this issue by building a multiclassclassification model trained on Faux HateMulti-Label Dataset (Biradar et al. 2024)which consists of hateful remarks that arefraudulent and have a code mix of Hindi andEnglish. Model has been built to classifySeverity (Low, Medium, High) and Target(Individual, Organization, Religion) on thedataset. Performance of the model isevaluated on test dataset achieved varyingscored for each. For Severity model achieves74%, for Target model achieves 74%. Thelimitations and performance issues of themodel has been understood and wellexplained.
%U https://aclanthology.org/2024.icon-fauxhate.4/
%P 16-21
Markdown (Informal)
[Challenges and Insights in Identifying Hate Speech and Fake News on Social Media](https://aclanthology.org/2024.icon-fauxhate.4/) (Murugan et al., ICON 2024)
ACL
- Shanthi Murugan, Arthi R, Boomika E, Jeyanth S, and Kaviyarasu S. 2024. Challenges and Insights in Identifying Hate Speech and Fake News on Social Media. In Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate), pages 16–21, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).