Unpacking Faux-Hate: Addressing Faux-Hate Detection and Severity Prediction in Code-Mixed Hinglish Text with HingRoBERTa and Class Weighting Techniques

Abstract

The proliferation of hate speech and fake narra-tives on social media poses significant societalchallenges, especially in multilingual and code-mixed contexts. This paper presents our systemsubmitted to the ICON 2024 shared task onDecoding Fake Narratives in Spreading Hate-ful Stories (Faux-Hate). We tackle the prob-lem of Faux-Hate Detection, which involvesdetecting fake narratives and hate speech incode-mixed Hinglish text. Leveraging Hin-gRoBERTa, a pre-trained transformer modelfine-tuned on Hinglish datasets, we addresstwo sub-tasks: Binary Faux-Hate Detection andTarget and Severity Prediction. Through the in-troduction of class weighting techniques andthe optimization of a multi-task learning ap-proach, we demonstrate improved performancein identifying hate and fake speech, as well asin classifying their target and severity. Thisresearch contributes to a scalable and efficientframework for addressing complex real-worldtext processing challenges.