Mitigating Abusive Comment Detection in T amil Text: A Data Augmentation Approach with Transformer Model (2023)

Abstract: With the increasing number of users on social media platforms, the detection and categorization of abusive comments have become crucial, necessitating effective strategies to mitigate their impact on online discussions. However, the intricate and diverse nature of lowresource Indic languages presents a challenge in developing reliable detection methodologies. This research focuses on the task of classifying YouTube comments written in Tamil language into various categories. To achieve this, our research conducted experiments utilizing various multi-lingual transformer-based models along with data augmentation approaches involving back translation approaches and other pre-processing techniques. Our work provides valuable insights into the effectiveness of various preprocessing methods for this classification task. Our experiments showed that the Multilingual Representations for Indian Languages (MURIL) transformer model, coupled with round-trip translation and lexical replacement, yielded the most promising results, showcasing a significant improvement of over 15 units in macro F1-score compared to existing baselines. This contribution adds to the ongoing research to mitigate the adverse impact of abusive content on online platforms, emphasizing the utilization of diverse preprocessing strategies and state-of-the-art language models.

## introduction abusive speech, a form of communication intending to harm or promote hatred against vulnerable individuals or groups based on factors like gender, racial background, religious beliefs, complexion, or physical capabilities, often employs offensive language. it can lead to severe psychological effects on the targeted people, potentially pushing them towards wrongful actions @xcite . with the widespread expansion of social networking sites such as facebook and twitter, the internet has become a hub for extensive information exchange. however, this digital boom has introduced challenges, particularly the rise in issues like hate speech and cyberbullying. among these, abusive comments stand out as a troubling concern, characterized by the use of offensive language against individuals or groups. online abuse has far-reaching effects, including diminished self-esteem, depression, widespread harassment, etc. identifying and addressing these comments is of utmost importance. effectively categorizing these comments not only aids in assessing their seriousness but also equips authorities with the means to undertake suitable measures against those responsible. our study concentrates on detecting abusive comments in tamil, a dravidian language spoken by the tamil community in south asia, which holds a special place as one of india's 22 scheduled languages @xcite . the limited linguistic resources of tamil present a hurdle for natural language processing, creating challenges in the acquisition of sufficient datasets. detecting abusive comments essentially involves text classification, which aims to categorize text into predefined classes or categories. while previous research has used transformer models @xcite for this purpose, data augmentation in tamil has been unexplored due to its status as a low-resource language and its inherent challenges. this paper focuses on conducting experiments employing various transformer-based models and exploring different data preprocessing and augmentation techniques. back translation methods such as round-trip translation and lexical replacement have also been explored. our primary aim was to identify the most effective model by analyzing the outcomes, focusing on addressing the critical challenge of combating abusive comments in the digital landscape of tamil-speaking communities. identifying online abusive content is a challenging task, particularly due to the hairline boundary between abusive language and free expression @xcite . the paper @xcite outlines methods for detecting hope speech in short, casual written content in english, malayalam, and tamil using a range of machine learning methods. it emphasizes that even simple algorithms can produce satisfactory outcomes when provided with sufficient training data. the most successful approach is cross-lingual transfer learning through fine-tuning xlm-roberta. the paper @xcite highlights the pressing need to swiftly detect and remove hate speech and offensive content from social platforms due to their rapid spread and detrimental effects. detecting hate speech poses a significant challenge, particularly in code-mixed languages like hindi-english, tamil-english, malayalam-english, and telugu-english. this comprehensive study investigates and contrasts various machine learning and deep learning techniques for addressing this issue. team centamil (prasanth et al., 2022) employed tf-idf with character-level word boundary analysis and the random kitchen sink (rks) algorithm to generate feature vectors for abusive comment detection in the tamil language. classification was performed using a support vector machine (svm) with a polynomial kernel. this method was applied to the tamil dataset, achieving the first rank with a macro f1-score of 0.32 @xcite . subsequently, a comprehensive comparison was conducted by @xcite to evaluate various machine learning algorithms with diverse feature extractors for detecting abusive comments in tamil and code-mixed tamil-english. the study by @xcite focuses on enhancing tamil text representation for hate speech detection. it explores various embedding techniques, including tf-idf and pre-trained transformer models. stemming algorithms are applied to address the linguistic complexity of tamil. experiments with classifiers like logistic regression, svm, stochastic gradient descent (sgd), decision trees, and ensemble models reveal that stemming improves performance, and muril, combined with majority voting, achieved the best results in classifying offensive content, considering data imbalance. ## dataset the abusive comment detection dataset by @xcite comprises comments written in the tamil language, sourced from youtube's comment section. this dataset includes comments and their respective labels categorized into nine distinct classes: misandry, misogyny, counter-speech, xenophobia, hope-speech, transphobic, homophobia, not-tamil, and none-of-theabove. the class counter-speech involves delivering feedback through fact-based arguments in a non-aggressive way, while hope speech encompasses optimistic narratives detailing how individuals cope with and overcome adversity. misogynistic class is aimed at women or particular gendered groups, while homophobic content constitutes a form of gender-based harassment utilizing disparaging labels. transphobic statements are phrases that are used to belittle vulnerable transgender individuals. xenophobia is characterized by fear of or hate for what is thought to be alien or odd is known as xenophobia @xcite . figure 1 illustrates the dataset split showcasing distinct classes and their percentages. the dataset has been divided into three sets: training, validation, and test, with 2240, 560, and 699 samples, respectively. each entry in the training dataset consists of a tamil text comment and its associated label. ## results and discussion the results obtained by various transformer models trained on raw data is shown in ## conclusion and future work our study presented promising insights into the effectiveness of data augmentation for abusive comment detection in a low-resource language like tamil. having observed an improvement in accuracy when employing two round-trip translations, the macro-averaged f1 score achieved its highest score with a single round-trip translation using english as an intermediate language. these results highlight data augmentation's potential to improve transformer-based models' performance in detecting abusive comments in low-resource languages, emphasizing the importance of selecting the right intermediate language for optimal results. the implementation details are shared in the github repository. 2 in the future, we would like to expand our investigation by incorporating a wider selection of intermediate languages in the round-trip translation process, allowing us to assess how these language choices influence the outcomes in diverse linguistic contexts on ensemble-based models.