Detecting Hate Speech and Fake Narratives in Code-Mixed H inglish Social Media Text (2024)

Abstract: The increasing prevalence of hate speech and fake narratives on social media platforms posessignificant societal challenges. This study ad-dresses these issues through the developmentof robust machine learning models for twotasks: (1) detecting hate speech and fake nar-ratives (Task A) and (2) predicting the targetand severity of hateful content (Task B) incode-mixed Hindi-English text. We proposefour separate CNN-BiLSTM models tailoredfor each subtask. The models were evaluatedusing validation and 5-fold cross-validationdatasets, achieving F1-scores of 74% and 79%for hate and fake detection, respectively, and63% and 54% for target and severity predic-tion and achieved 65% and 57% for testingresults. The results highlight the models’ effec-tiveness in handling the nuances of code-mixedtext while underscoring the challenges of under-represented classes. This work contributes tothe ongoing effort to develop automated toolsfor detecting and mitigating harmful contentonline, paving the way for safer and more in-clusive digital spaces.

## introduction the icon 2024 shared task on decoding fake narratives in spreading hateful stories (faux-hate) 1 @xcite addresses a critical issue in the digital age: the intersection of fake narratives and hate speech. while enabling global connectivity, social media platforms have become hotspots for rapidly disseminating harmful and misleading content. this toxic combination exacerbates societal divisions, fosters animosity, and often leads to real-world consequences. the shared task emphasizes the need for innovative approaches to detect and counter this dual threat, particularly in code-mixed hindi-english (hinglish) text, which poses unique linguistic challenges. faux-hate refers to the spread of hate speech rooted in fake narratives and misinformation deliberately constructed or shared to provoke hostility and divide communities. these instances often blend falsehoods with emotionally charged language, making them more potent and harder to detect. addressing this problem requires a holistic approach to discerning hate speech and misinformation within diverse and noisy online environments. we employed a cnn-bilstm @xcite architecture to tackle the challenge for this shared task. this hybrid model combines the strengths of convolutional neural networks (cnns) @xcite and bidirectional long shortterm memory (bilstm) @xcite networks. cnns are adept at capturing local features and patterns within the text, while bil-stms excel in modelling sequential dependencies and contextual relationships. this combination makes the model well-suited for processing complex, code-mixed text, where both local nuances and global context play a critical role. the faux-hate shared task is designed to: with the increasing prevalence of code-mixed content on social platforms, traditional nlp models often struggle to deliver accurate predictions due to linguistic diversity and noise. by integrating advanced architectures like cnn-bilstm, this task aims to push the boundaries of what's possible in multilingual and multimodal hate speech detection. this shared task contributes to a growing body of research to mitigate the harms caused by toxic and misleading online narratives, ensuring safer digital spaces for all. ## literature survey the detection of hate speech and fake narratives has advanced significantly with the adoption of machine learning (ml) and deep learning (dl) techniques. early work predominantly relied on traditional ml approaches, such as support vector machines (svms) and naïve bayes, combined with feature engineering techniques like term frequencyinverse document frequency (tf-idf) and n-grams @xcite . while effective for smaller datasets, these methods struggled with contextual understanding and often failed to generalize across languages and nuanced categories, such as implicit hate or sarcasm. latent dirichlet allocation (lda) has also been applied to uncover hidden topics within datasets, aiding in identifying hate-related themes @xcite . deep learning approaches have significantly improved performance in this domain. convolutional neural networks (cnns) are effective for capturing local patterns in text, while long short-term memory (lstm) networks excel in modelling sequential data @xcite . hybrid architectures, such as cnn-bilstm, leverage the strengths of both frameworks, offering robust solutions for tasks involving code-mixed text, including hindi-english datasets @xcite . furthermore, transformerbased architectures like bert and roberta have set new benchmarks in detecting hate speech and fake narratives by leveraging pre-trained language models for better contextual understanding (kenton and toutanova, 2019). these models are particularly powerful in multilingual and low-resource settings, often fine-tuned on hate speech datasets to enhance domain-specific performance @xcite . recent advancements include the use of genera-tive adversarial networks (gans) for data augmentation, which help mitigate class imbalance and improve robustness @xcite . multimodal approaches, integrating textual and visual inputs, have been employed for tasks like detecting hateful memes and fake narratives involving images @xcite . however, challenges persist in explainability, as deep learning models often act as "black boxes." this has motivated researchers to integrate explainable ai (xai) techniques, ensuring more interpretable and ethical model predictions @xcite . despite these challenges, the field continues progressing, with innovations targeting online content's nuanced and multilingual nature. ## dataset the dataset for the faux-hate @xcite shared task has been meticulously curated to reflect real-world challenges in detecting fake narratives and hate speech within code-mixed hindi-english social media text. it consists of three files shared across the training, validation, and test phases, supporting both task a: binary faux-hate detection and task b: target and severity prediction. for task a, each text sample in the dataset is labelled with two binary annotations: fake, indicating whether the content is fake (1) or real (0), and hate, indicating whether the content contains hate speech (1) or not (0). task b extends this with additional labels: target, identifying the intended target of the hate speech as either individual (i), organization (o), or religion (r), and severity, specifying the intensity of the hate speech as low (l), medium (m), or high (h). the dataset is released in three phases to facilitate model development and evaluation. details of the dataset distribution are presented in ## methodology in this work, we propose two separate deep learning models for addressing the tasks of detecting fake narratives and hate speech in code-mixed hindi-english social media text. both models are based on the cnn-bilstm architecture 2 as shown in figure 1 , known for capturing local features and contextual dependencies in sequential data. the methodology for each task is described below. ## results the proposed cnn-bilstm models were evaluated on the validation dataset and through 5-fold cross-validation to assess their performance on the tasks of hate and fake detection (task a). the results demonstrate the models' ability to effectively handle the challenges posed by code-mixed hindi-english social media text. ## conclusion this work addresses the critical challenge of detecting hate speech and fake narratives in codemixed hindi-english social media text and predicting the target and severity of hateful content. we proposed four separate cnn-bilstm models tailored for these tasks, demonstrating effective performance on validation and cross-validation datasets. for task a, the hate and fake detection models achieved consistent results, with f1-scores of 74% and 79%, respectively, across 5-fold crossvalidation. the robustness of the models was evident in their ability to generalize well on the validation set, with minimal performance drop. for task b, the target and severity prediction models achieved f1-scores of 63% and 54%, respectively, highlighting the complexities involved in handling multi-class classification tasks, especially for underrepresented categories. the results underscore the challenges inherent in processing code-mixed text, particularly when dealing with nuanced tasks such as identifying fake narratives and varying levels of hate intensity. the models' performance demonstrates the potential of deep learning techniques to tackle such problems while also revealing areas for improvement, such as enhancing class representation for underrepresented categories like individual, religion, high, and low.