Unpacking Faux-Hate: Addressing Faux-Hate Detection and Severity Prediction in Code-Mixed H inglish Text with H ing R o BERT a and Class Weighting Techniques (2024)

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.

## introduction social media has revolutionized communication but has also created a breeding ground for harmful content, including hate speech. the combination of hate speech and fake narratives termed faux-hate poses a unique challenge as it exploits misinformation to provoke or mislead, amplifying its impact. addressing this requires advanced models capable of understanding nuanced, codemixed language, such as hinglish (hindi-english). this study focuses on developing a system to @xcite and classify its attributes within social media text. using hin-groberta, a state-of-the-art transformer model, we target two primary objectives: ## related work the intersection of hate speech and fake news detection has drawn increasing attention, especially since studies in 2016 linked hate speech propagation to fabricated narratives @xcite . during the u.s. presidential elections, 37 out of 49 articles on hate speech were based on fabricated content. however, significant gaps remain, particularly for low-resource languages like hinglish. existing datasets like hostile post detection in hindi @xcite , factdril @xcite , iehate @xcite , heot @xcite , and hesoc @xcite focus on either fake or hate detection, often neglecting their intersection. most also emphasize binary hate detection, overlooking aspects like target or severity. the faux hate multi-label dataset (fhmld) bridges these gaps, offering multi-class annotations for hate speech target and severity in hinglish, while linking fake narratives with hate speech. ## conclusion this study tackled the icon 2024 shared task on decoding fake narratives in hateful stories (faux-hate), focusing on detecting fake content and hate speech in hinglish. using hingroberta with class weighting and multi-task learning, we achieved promising results. task a yielded macro f1 scores of 0.8199 for fake detection and 0.7684 for hate detection, while task b achieved 0.6938 for target prediction and 0.5643 for severity classification, with challenges in distinguishing adjacent severity levels and minority classes. confusion matrix analysis showed strong performance in hate detection and organizational targets but highlighted areas for improvement, particularly in nuanced categories. future work could explore advanced architectures and techniques to address class imbalances and evolving linguistic trends in code-mixed text.