Decoding Fake Narratives in Spreading Hateful Stories: A Dual-Head R o BERT a Model with Multi-Task Learning (2024)

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.

## introduction social media has revolutionized communication, providing unprecedented connectivity across the globe. this increased connectivity, however, has also inadvertently fostered the rapid dissemination of harmful content, including the troubling combination of hate speech and fabricated narratives. hate speech, particularly when intertwined with falsehoods, exacerbates its detrimental impact, fueling discrimination, violence, and societal unrest. in response to this growing concern, the faux-hate shared task @xcite , based on a phenomenon recently characterized and dataset curated by @xcite , introduces a unique challenge: identifying and categorizing instances of hate speech generated through fake narratives in code-mixed hindi-english text. this task emphasizes the importance of detecting and analyzing content that misleads and provokes through a combination of misinformation and hateful language. researchers and practitioners have increasingly turned their attention to understanding and combating these complex phenomena. the shared task comprises two sub-tasks: task a focuses on binary classification of fake and hate labels, while task b involves predicting the target and severity of hateful content. this paper describes our system, methodologies, and experimental results for both sub-tasks, contributing to the broader effort to address hate speech and fake narratives in multilingual, code-mixed contexts. ## related work the detection of hate speech and misinformation on social media has been a prominent area of research within natural language processing (nlp). studies have extensively explored techniques for identifying hate speech across various languages and platforms @xcite , often leveraging machine learning and deep learning approaches. recent advancements include transformer-based models like bert @xcite , roberta, and multilingual bert (mbert), which have shown significant success in text classification tasks, including hate speech detection. fake news and misinformation detection have similarly gained attention @xcite , with methods ranging from linguistic feature analysis to neural network-based classification. the intersection of hate speech and fake narratives, however, remains a relatively unexplored domain, particularly in code-mixed languages like hindi-english. prior work in code-mixed text processing has highlighted the challenges posed by nonstandard grammar, orthographic variations, and the lack of annotated datasets. this shared task builds on these research threads, offering a novel opportunity to investigate faux-hate in a multilingual and culturally nuanced context. our approach draws inspiration from prior work in hate speech and fake news detection while tailoring solutions to the unique challenges of the code-mixed hindi-english dataset provided in this task. ## methodology this section outlines the architecture, components, and training methodology of the dual-head roberta model developed for the faux-hate shared task. our system leverages robertabase @xcite as the backbone encoder and extends it with a dual-head classification mechanism for simultaneous hate speech and fake news detection. the architecture adopts a multitask learning approach @xcite , enabling the model to effectively share information across tasks while maintaining task-specific parameterization through dedicated classification heads. the code for our system, including implementation details and pre-trained models, is available at our github repository: https://github.com/ yash9439/icon-faux-hate-shared-task . ## experiments and results this section outlines the experimental setup, training procedure, evaluation metrics, and the results obtained for both tasks in the faux-hate shared task. additionally, we analyze the impact of architectural variations, specifically the inclusion and exclusion of residual connections in the classification heads. ## analysis for task a, which involved binary classification of faux-hate instances into hate speech or fake content, we evaluated our model's performance based on standard classification metrics. the results, as shown in the analysis of these results underscores the importance of task-specific fine-tuning, particularly for the classification heads, in achieving high performance. the shared model architecture also proved beneficial in efficiently utilizing training data, thereby improving outcomes for both tasks. future work could focus on refining fine-tuning strategies and incorporating more diverse datasets to enhance the model's robustness in real-world applications. ## conclusion we presented a dual-head roberta model for the faux-hate shared task, addressing both binary classification of fake and hate speech (task a) and multiclass classification of target and severity (task b). our system achieved competitive results, demonstrating the effectiveness of multitask learning in handling the complexities of code-mixed hindi-english text. the model showed strong performance on both tasks, and future work will focus on refining the model with additional data and finetuning techniques to further improve its accuracy.