Challenges and Insights in Identifying Hate Speech and Fake News on Social Media (2024)

Abstract: Social media has transformed communication, but it has also brought abouta number of serious problems, most notablythe proliferation of hate speech and falseinformation. hate-related conversations arefrequently fueled by misleading narratives.We address this issue by building a multiclassclassification model trained on Faux HateMulti-Label Dataset (Biradar et al. 2024)which consists of hateful remarks that arefraudulent and have a code mix of Hindi andEnglish. Model has been built to classifySeverity (Low, Medium, High) and Target(Individual, Organization, Religion) on thedataset. Performance of the model isevaluated on test dataset achieved varyingscored for each. For Severity model achieves74%, for Target model achieves 74%. Thelimitations and performance issues of themodel has been understood and wellexplained.

## introduction social media platforms make it easier to communicate and share information, eventually we could see hate speech and fake news in social media posts, comments and blogs. digital platforms have serious problems from hate speech, which aims to marginalize individuals or groups based on characteristics like religion, race, or political ideas, and fake news, which is defined by intentional disinformation intended to deceive. these problems worsen social and political conflicts in addition to lowering the caliber of information that is accessible online. natural language processing (nlp) extends its application over social media narratives to address these issues by classifying them. we build a classification system with multinomial naive bayes (mnb), a robust machine learning model for classification, has shown effectiveness in managing highdimensional data, including text. we found mnb is a desirable option for addressing issues like hate speech identification and false news. 1) task 1: we use multinomial naive bayes (mnb) to classify hate and disinformation content. 2) task 2: the same mnb algorithm with different preprocessing method is used for predicting targets, target class includes individual, groups, and region. and the intensity of hate speech. the results highlight the significance of lightweight and interpretable models in practical applications while also enhancing the accuracy of automated systems intended to protect digital areas. ## related work recent research highlights the intersection of hate speech and fake news, particularly in low-resource languages like hindi-english code-mixed texts, which remain underexplored. while datasets like those from @xcite and factdril @xcite have addressed hate speech and fake news in hindi separately, efforts to bridge both phenomena in multilingual contexts are limited. contributions such as bohra et al.'s (2018) binary classification dataset for hate speech in hindi-english codemixed text andw mathur et al.'s (2018) heot dataset address class imbalance and real-world representation issues but remain focused on monolingual or binary tasks. the faux hate multi-label dataset (fhmld) aims to address these gaps by incorporating multi-label and multi-class annotations, enabling advanced models to tackle both hate speech and fake news simultaneously in hindi-english code-mixed texts. deep learning and transformer-based approaches, such as cnn-lstm with embeddings (mathur et al., 2018; fharook et al., 2022), rnn-based models (bisht et al., 2020), and refined transformers like mbert and xml-r (banerjee et al., 2021; farooqi et al., 2021), have shown promise in handling such texts. however, cross-lingual and context-aware models, including bert, elmo, and flair, have achieved macro f1 scores of around 0.71 but are yet to address the dual challenge effectively. ## dataset resource and data processing to study issues such as fake news, hate speech, and harmful content, we utilized existing datasets curated from environments known for the rapid spread of misinformation and harmful rhetoric, provide a robust foundation for analyzing these critical issues. the work by @xcite introduces a code-mixed dataset (hindi -english) as part of the shared task on "decoding fake narratives in spreading hateful stories (faux-hate)." this dataset focuses on identifying and analyzing fake narratives intertwined with hateful rhetoric, making it a valuable resource for studying misinformation and hate speech in natural language. ## conclusion the overlap between hate speech and fake news in social media narratives necessitates integrated approaches for detection and mitigation. our work bridges this gap by presenting a classification framework utilizing a novel hindi-english codemixed dataset, enabling nuanced categorization based on severity and targets. despite achieving promising results, challenges remain in scaling to multilingual contexts, addressing computational constraints, and refining interpretability for realworld applications. these findings highlight the importance of continued research and dataset evolution to combat hate speech and misinformation effectively.