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{
"anthology_id": "2024.icon-fauxhate.1",
"abstract": "Social media has undeniably transformed how people communicate; however, it also brings significant drawbacks, particularly the proliferation of fake and hateful comments. Recent observations indicate that these issues frequently coexist. In this context, we propose the shared task of detecting the role of fake narratives in spreading hateful stories, termed ``Faux-Hate\". We also introduce a novel dataset, FEUD, comprising fake-instigated hateful comments in Hindi-English code-mixed text. A total of 22 teams from various institutes registered for the shared task. Twelve teams participated in Task A, which focuses on binary classification, and ten teams participated in Task B, which involves more fine-grained classification with multi-task learning. Among the participating teams, DCST\\_unigoa secured the first rank with a Macro F1 score of 0.79."
}
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