Faux-Hate Multitask Framework for Misinformation and Hate Speech Detection in Code-Mixed Languages (2024)

Abstract: The Faux-Hate task looks at two big issues:misinformation and hate speech. It focuses onHindi-English social media posts. This papershares our methods for both parts of the task.For Task A, we built a special model based onXLM-RoBERTa. It has features that help usspot both fake news and hate speech at the sametime. For Task B, we wanted to identify whothe hate is aimed at (like individuals or groups)and how severe it is (high, medium, low). So,we added different tools to our model for thiskind of sorting. To get ready for all this, wecarefully cleaned the data, especially dealingwith mixing languages and different spellings.In Task A, our results show that our model canclearly tell the difference between fake and realstories, as well as between hate and non-hatespeech. For Task B, it does a good job withidentifying targets and severity levels, givingstrong predictions for multiple labels. Overall,these results show that cross-lingual models,combined with specific tweaks, can really helptackle complex text classification in languageswith fewer resources.

## introduction social media has transformed the way of communication and allowed for global interaction but also become a channel for fake narratives and hate speech, which disrupts social cohesion. in linguistically diverse regions like india, where code-mixed languages such as hindi-english are common, the complexities of transliteration and cultural nuances demand advanced approaches to handle the contextual dependencies of multilingual, low-resource text. the two subtasks of the faux-hate shared task at icon 2024 serve to bridge this challenge: task a involves binary classification-classifying between fake/real and hate/non-hate speech. task b calls for multi-class predictions for the target of hate (individual, organization, religion, or n/a) and the severity (low, medium, high, or n/a). both tasks raise challenging constraints: skewed class distributions in task a make it hard to generalize well across underrepresented classes, such as fake but not hateful content; and task b requires deep understanding since implicit targets often rely on sarcasm or cultural allusions @xcite . we try to overcome these challenges using multitask models based on the latest state-of-the-art cross-lingual transformer pre-trained on more than 100 languages, namely xlm-roberta. to accomplish this, for task a, we predict the fake and hate labels simultaneously with a dual-output architecture. for task b, we extend the model with taskspecific attention mechanisms in order to really boost the focus of the model on relevant features for each prediction task at hand. preprocessing steps such as handling blank labels and removing transliteration inconsistencies improve the ability of the models to process complex code-mixed text. this work is an extension of earlier research in the class of multilingual hate speech detection and misinformation classification. for example, frameworks such as hold-z, developed especially for telugu-english code-mixed text, show effectiveness in merging contextual embeddings with task-specific attention layers for low-resource scenarios @xcite . likewise, there are studies on multilingual pre-trained models such as mbert and xlm-roberta, where their strength to capture cross-lingual patterns emerges as suitable for code-mixing scenarios @xcite . for example, while @xcite suggest that language-specific fine-tuning is key in improving performance over lowresource indian languages, @xcite explore ensemble methods for the detection of offensive content in youtube comments. attention mechanisms, particularly selfattention in transformer architectures, have significantly improved contextual understanding by focusing on key tokens in input sequences @xcite . in hate speech and misinformation detection, attention layers help extract relevant features while filtering out noise. for example, it was shown by @xcite for context-dependent hate speech that hierarchical attention is effective in multi-label classification. @xcite used multi-head attention to enhance feature extraction, especially in multilingual environments with code mixing. both of these results confirm this work's use of specific task attention layers to properly handle contextual nuances and improve classifications. using task-specific augmentations and preprocessing, our models reached macro f1 scores of 0.78 for fake, 0.75 for hate, 0.66 for target, and 0.75 for severity. this indicates the effectiveness of our method in dealing with misinformation and hate speech in code-mixed contexts while enabling scalable multilingual content moderation. ## methodology this section describes the approach adopted towards solving the faux-hate shared task, focusing on identifying fake and hate speech within social media comments. initially, a brief introduction to the problem statement and dataset is presented as it is. consequently, approaches used to address this challenges have been considered. ## results this section presents the performance evaluation of the proposed multi-task shared attention model for both tasks, task a (faux-hate detection) and task b (target and severity prediction), in comparison with a siamese network model for task a. the performance is measured using the macro f1-score, a robustic metric for assessing model effectiveness in class-imbalanced datasets. the macro f1-score provides an average f1-score across all classes, treating each class equally, regardless of its prevalence in the dataset. ## conclusion and future work attention mechanisms play a very important role in multi-task learning, especially for the complex task of text classification in areas such as detecting misinformation and hate speech. our shared-attentionbased multi-task model, which used task-specific attention layers, efficiently classified comments as fake or hate and predicted the target and severity. the model won task a over the siamese network with macro f1 scores of 0.80 on fake detection and 0.76 on hate detection, pushing it up to 4th rank in the rankings, though was merely moderate in task b with f1 scores of 0.45 for target classification and 0.50 for severity prediction ranking 5th, which really illustrated challenges related to the complexity and difficulty of the task. the experiments clearly show that shared attention mechanisms play a significant role in identifying the key features across tasks, which greatly contributed to the model's success in task a. however, moderate performance in task b suggests further exploration is needed. future work could focus on enhancing the granularity of attention mechanisms, incorporating pre-trained language models tailored to code-mixed text, and exploring additional contextual features to improve target and severity prediction. additional to this, experimenting with hierarchical or task-specific encoders might further refine the shared attention framework and improve performance on all tasks.