@inproceedings{bhaskar-etal-2024-decoding,
    title = "Decoding Fake Narratives in Spreading Hateful Stories: A Dual-Head {R}o{BERT}a Model with Multi-Task Learning",
    author = "Bhaskar, Yash  and
      Bahad, Sankalp  and
      Krishnamurthy, Parameswari",
    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/fix-sig-urls/2024.icon-fauxhate.3/",
    pages = "12--15",
    abstract = "Social media platforms, while enabling globalconnectivity, have become hubs for the rapidspread of harmful content, including hatespeech and fake narratives (Davidson et al.,2017; Shu et al., 2017). The Faux-Hateshared task focuses on detecting a specific phe-nomenon: the generation of hate speech drivenby fake narratives, termed Faux-Hate. Partici-pants are challenged to identify such instancesin code-mixed Hindi-English social media text.This paper describes our system developed forthe shared task, addressing two primary sub-tasks: (a) Binary Faux-Hate detection, involv-ing fake and hate speech classification, and(b) Target and Severity prediction, categoriz-ing the intended target and severity of hate-ful content. Our approach combines advancednatural language processing techniques withdomain-specific pretraining to enhance perfor-mance across both tasks. The system achievedcompetitive results, demonstrating the efficacyof leveraging multi-task learning for this com-plex problem."
}