@inproceedings{d-joy-srivastava-2024-rejected,
    title = "Rejected Cookies @ Decoding Faux-Hate: Predicting Fake Narratives and Hateful Content",
    author = "D Joy, Joel  and
      Srivastava, Naman",
    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.7/",
    pages = "36--39",
    abstract = "This paper reports the results of our team for theICON 2024 shared task Decoding Fake Narra-tives in Spreading Hateful Stories (Faux-Hate).The task aims at classifying tweets in a multi-label and multi-class framework. It comprisestwo subtasks: (A) Binary Faux-Hate Detec-tion, which involves predicting whether a tweetis fake (1/0) and/or hate speech (1/0, and (B)Target and Severity Prediction, which cate-gorizes tweets based on their target (Individ-ual, Organization, Religion) and severity (Low,Medium, High). We evaluated Machine Learn-ing (ML) approaches, including Logistic Re-gression, Support Vector Machines (SVM), andRandom Forest; Deep Learning (DL) methods,such as Artificial Neural Networks (ANN) andBidirectional Encoder Representations fromTransformers (BERT); and innovative quantumhybrid models, like Hybrid Quantum NeuralNetworks (HQNN), for identifying and classi-fying tweets across these subtasks. Our exper-iments trained and compared multiple modelarchitectures to assess their comparative per-formance and detection capabilities in these di-verse modeling strategies.The best-performingmodels achieved F1 scores of 0.72, 0.76, 0.64,and 0.54 for the respective labels Hate, Fake,Target and Severity. We have open-sourced ourimplementation code for both tasks on Github1 ."
}