Shared Feature-Based Multitask Model for Faux-Hate Classification in Code-Mixed Text (2024)

Abstract: In recent years, the rise of harmful narratives online has highlighted the need for advancedhate speech detection models. One emergingchallenge is the phenomenon of Faux Hate, anew type of hate speech that originates fromthe intersection of fake narratives and hatespeech. Faux Hate occurs when fabricatedclaims fuel the generation of hateful language,often blurring the line between misinforma-tion and malicious intent. Identifying suchspeech becomes especially difficult when thefake claim itself is not immediately apparent.This paper provides an overview of a sharedtask competition focused on detecting FauxHate, where participants were tasked with de-veloping methodologies to identify this nu-anced form of harmful speech.

## introduction the rapid increase in social media platform users has changed how information is created, shared, and consumed. this has significantly boosted both the volume and speed of information dissemination. social media has become a crucial tool for modern communication, providing instant access to realtime updates and facilitating seamless content sharing. while this shift has made information more accessible and enhanced global connectivity, it has also led to challenges, particularly the spread of misinformation and disinformation @xcite . among the most concerning forms of misinformation is fake news, which refers to deliberately fabricated or misleading content presented as legitimate news. the rapid spread of fake news is often exacerbated by social media algorithms, which prioritize engagement metrics over the accuracy of the information @xcite . the situation becomes even more alarming when fake news intersects with hate speech, forming a potent combination known as "fake instigated hate news" @xcite . this category encompasses fabricated or distorted content designed to incite hostility, discrimination, or violence against individuals or groups based on attributes such as race, religion, gender, or political beliefs. hate speech, as defined by various legal and academic frameworks, refers to any speech, gesture, conduct, writing, or display that incites violence or prejudicial actions against others based on their identity or group membership @xcite @xcite . it perpetuates harmful stereotypes, dehumanizes marginalized groups, and exacerbates societal divisions. the combination of fake news and hate speech can amplify social tensions, leading to real-world consequences such as violence, discrimination, and the erosion of social trust. its widespread dissemination complicates efforts to maintain public safety and social cohesion, making it crucial to develop robust mechanisms for its detection and regulation @xcite . this dual threat of misinformation and hate speech creates unique challenges for law enforcement, policymakers, and researchers. such content is not only deceptive but also emotionally provocative, often capitalizing on societal tensions. the deliberate creation and spread of such material threaten the social fabric, underscoring the urgent need for effective detection and mitigation mechanisms. in multilingual societies like india, code-mixed text-a blend of two or more languages within a single discourse-adds an additional layer of complexity to identifying fake news and hate speech @xcite . hindi-english code-mixed communication is particularly prevalent on social media platforms, where users frequently switch between languages for expression and convenience. the informal and inconsistent nature of code-mixed text, transliteration, and frequent spelling variations pose significant challenges for traditional natural language processing (nlp) techniques. addressing these challenges requires sophisticated computational approaches that can effectively capture the linguistic nuances of such hybrid text. this study proposes a multitask learning model that leverages shared linguistic features to simultaneously classify code-mixed tweets as hate vs. non-hate and fake vs. non-fake. by focusing on this multi-task framework, the study aims to advance the development of effective tools to mitigate the spread of harmful content on social media. ## methodology this study presents a multitasking classification model to address the challenges of detecting hate speech and fake content in hindi-english codemixed text. the methodology combines effective preprocessing techniques with fasttext embeddings to create a shared vector space that captures code-mixed data's semantic and syntactic nuances. these shared embeddings serve as input to a multitask framework comprising two independent support vector machine (svm) classifiers. these classifiers are designed to simultaneously categorize tweets as hate vs. non-hate and fake vs. non-fake. this approach utilizes shared linguistic features across the tasks to improve classification accuracy and computational efficiency. ## results and discussion in this section, we present the results obtained for the multitasking model developed for identifying binary hate and fake tweets simultaneously for subtask a. organizers have used macro f1 score to evaluate the labels presented by the participants for the test dataset. the proposed multitasking model's performance on the faux-hate shared task dataset demonstrates its ability to effectively classify binary hate or non-hate and fake and non-fake labels. ## conclusion this work presents a multitasking model for identifying hate and fake content within the faux hate shared task dataset. the proposed approach leverages fasttext embeddings with a shared feature space to address binary hate and fake classification tasks simultaneously. the model demonstrated competitive performance on validation and test datasets, achieving a macro f1-score of 0.7445 on the test data and ranking 10th in the competition. these results underscore the potential of multitasking architectures in tackling complex, multi-faceted problems in natural language processing, particularly for code-mixed and informal text.