@inproceedings{vetagiri-pakray-2024-detecting,
    title = "Detecting Hate Speech and Fake Narratives in Code-Mixed {H}inglish Social Media Text",
    author = "Vetagiri, Advaitha  and
      Pakray, Partha",
    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://preview.aclanthology.org/ingest-emnlp/2024.icon-fauxhate.5/",
    pages = "22--28",
    abstract = "The increasing prevalence of hate speech and fake narratives on social media platforms posessignificant societal challenges. This study ad-dresses these issues through the developmentof robust machine learning models for twotasks: (1) detecting hate speech and fake nar-ratives (Task A) and (2) predicting the targetand severity of hateful content (Task B) incode-mixed Hindi-English text. We proposefour separate CNN-BiLSTM models tailoredfor each subtask. The models were evaluatedusing validation and 5-fold cross-validationdatasets, achieving F1-scores of 74{\%} and 79{\%}for hate and fake detection, respectively, and63{\%} and 54{\%} for target and severity predic-tion and achieved 65{\%} and 57{\%} for testingresults. The results highlight the models' effec-tiveness in handling the nuances of code-mixedtext while underscoring the challenges of under-represented classes. This work contributes tothe ongoing effort to develop automated toolsfor detecting and mitigating harmful contentonline, paving the way for safer and more in-clusive digital spaces."
}