DLRG - D ravidian L ang T ech@ EACL 2024 : Combating Hate Speech in T elugu Code-mixed Text on Social Media (2024)

Abstract: Detecting hate speech in code-mixed language is vital for a secure online space, curbing harmful content, promoting inclusive communication, and safeguarding users from discrimination. Despite the linguistic complexities of code-mixed languages, this study explores diverse pre-processing methods. It finds that the Transliteration method excels in handling linguistic variations. The research comprehensively investigates machine learning and deep learning approaches, namely Logistic Regression and Bi-directional Gated Recurrent Unit (Bi-GRU) models. These models achieved F1 scores of 0.68 and 0.70, respectively, contributing to ongoing efforts to combat hate speech in code-mixed languages and offering valuable insights for future research in this critical domain.

## introduction the surge in hateful speech online challenges maintaining respectful discourse. hate speech, involving hostility or discrimination, has profound implications for social harmony. digital platforms invest heavily in hate detection models to automate content flagging and removal, aiming to curb its spread. addressing hate speech in telugu codemixed language is a growing concern due to the rapid adoption of digital platforms by the indian population. automating hate speech detection is feasible for widely adopted languages like english, with ample models and labeled data. however, applying the same processes to niche languages like telugu, tamil, malayalam, etc., remains unexplored due to complexities and nuances, making it a more expensive endeavor. the demand for automated hate speech detection in code-mix languages is underscored by the infeasibility of the conventional manual review approach for low-resourced languages in handling the vast amount of digital data. in this study, two distinct models, the bi-gru and logistic regression, were carefully chosen to address the complexities of hate speech detection in code-mixed telugu language. the bi-gru, a deep learning model, excels in capturing intricate contextual relationships, leveraging its ability to analyze sequences of data bidirectionally. this is particularly advantageous for understanding the nuanced linguistic structures present in code-mixed languages. on the other hand, logistic regression, a machine learning model, proves efficient in utilizing linguistic features, word embeddings, and statistical patterns. these models aims to harness the strengths of both paradigms, allowing for a comprehensive and nuanced approach to hate speech classification. these techniques reflects a thoughtful strategy to effectively tackle the multifaceted nature of hate speech detection in telugu code-mixed languages in social media. ## related works dealing with challenges in low-resource languages such as dravidian languages involves addressing class imbalances as a major concern. these challenges were addressed by generating synthetic data through paraphrasing, utilizing the pega-sus fine-tuned model, and employing backtranslation with the m2m100 neural machine translation model @xcite . a study on part-of-speech (pos) tagging for codemixed english-telugu social media text tackled challenges in combining elements from different languages. classifiers like linear svms, crfs, and multinomial bayes, with varied feature combinations, were evaluated. crf outperformed svms and bayes classifiers in this context @xcite . hate speech and offensive content detection in malayalam and tamil code-mixed text used the hasoc-fire 2021 dataset. the muril model achieved the best performance with a weighted f1-score of 0.636 for tamil and 0.734 for malayalam @xcite . advanced multilingual transformer models, adopting a unique fine-tuning approach with learning rate scheduling based on macro f1-scores (ghosh @xcite , have shown success in identifying hate speech. the mbert-gru framework for hate speech detection in multilingual societies outperforms monolingual and state-of-the-art methods @xcite . the rise of hate speech on social media calls for automated detection using nlp models. integrating convolutional and recurrent layers yields 77.16% accuracy in identifying hate speech @xcite . multinomial logistic regression for hate speech on twitter achieves an average precision of 80.02%, recall of 82%, and accuracy of 87.68% @xcite . hate speech detection in bengali comments, with a dataset of 7,425 comments, successfully addresses challenges. the attention mechanism surpasses other algorithms with 77% accuracy @xcite . the exploration of abusive comment detection within the tamil+english dataset involved the utilization of random forest, resulting in a weighted average f1-score of 0.78 @xcite . the random forest classifier exhibited a notable performance in the hate speech and offensive content identification in marathi and hindi tweet datasets by achieving a macro f1 score of 75.19% and 73.12% @xcite . earlier study @xcite explored term weighting methods aimed at selecting pertinent url features and assessing their influence on the effectiveness of url classification, extending beyond the realm of text classification . in the domain of multilingual social media content, a novel relevance-based metric was introduced through the application of a statistics-based approach, facilitating the swift processing of multilingual queries @xcite . as a progressive phase in social media data analysis, multimodal face emotion recognition on code-mixed tamil memes was conducted by applying convolutional neural network (cnn) with an efficiency of 0.3028 @xcite . for sentiment analysis, various deep learning methods were applied @xcite . in tamil hate and offensive content identification, the role of stemming and stop words were analysed in @xcite 3 methodology ## results and discussion both proposed models for the classification task were studied and the results are discussed below. from ## conclusion classifying code-mixed, low-resource dravidian languages like telugu in social media is challenging due to the availability of limited labeled data, diverse language variations, and informal expressions. ambiguous language use and the absence of standardized resources make building effective models difficult, requiring tailored approaches for accurate sentiment and content analysis. logistic regression and bi-gru for telugu code-mix hate classification effectively capture complex patterns, enhancing contextual understanding for nuanced hate speech detection in telugu code-mix. refining fine-tuning and pre-processing techniques can further improve model efficacy.