Lidoma@ LT - EDI 2024: T amil Hate Speech Detection in Migration Discourse (2024)

Abstract: The exponential rise in social media users has revolutionized information accessibility and exchange. While these platforms serve various purposes, they also harbor negative elements, including hate speech and offensive behavior. Detecting hate speech in diverse languages has garnered significant attention in Natural Language Processing (NLP). This paper delves into hate speech detection in Tamil, particularly related to migration and refuge, contributing to the Caste/migration hate speech detection shared task. Employing a Convolutional Neural Network (CNN), our model achieved an F1 score of 0.76 in identifying hate speech and significant potential in the domain despite encountering complexities. We provide an overview of related research, methodology, and insights into the competition’s diverse performances, showcasing the landscape of hate speech detection nuances in the Tamil language.

## introduction the surge in social media platform users has led to a significant increase in information dissemination, granting immediate access to updated information with just a click. these platforms are used not only for social interaction but also for leisure and information retrieval @xcite . there has been a notable surge in interest in social media analysis tasks within nlp @xcite , with a focus on emerging fields like identifying hopeful speech, there is a growing emphasis on advancing in this direction. @xcite language identification @xcite , fake news @xcite , sentiment analysis @xcite , and hate speech @xcite ) that researchers experimented with diverse models, including deep learning @xcite , transformers @xcite , and traditional machine learning techniques @xcite . however, along with its advantages, the widespread adoption of social media (bade and afaro, 2018) also brings negative aspects @xcite . users sometimes exhibit behavior that can be harmful, offensive, and even hateful toward various segments of society @xcite . describing hate speech is complex, as andrew sellars argues against oversimplification of its definition and addressing methods @xcite . there's disagreement regarding how hate speech refers to groups, with certain definitions associating it with minority groups or specific characteristics like race, religion, gender, or sexual orientation @xcite . this challenge has led to several shared tasks focused on detecting hate speech. in this context, our article centers on the analysis of tamil user comments about migration and refuge using qualitative content analysis. as part of this effort, we participated in the caste/migration hate speech detection shared task @xcite , which aims to develop models capable of identifying hate speech related to caste or migration. the objective of this task is to create an automated classification system that predicts whether text, particularly on social media, contains caste/migration-related hate speech. we employed a cnn model for prediction, leveraging its successful track record in text classification within the literature. @xcite . our proposed model obtained an f1 score of 0.76, yielding promising performance on the task of binary hate speech detection. ## related work motivated by the linguistic diversity across india, where languages like tamil, telugu, kannada, malayalam, hindi, punjabi, bengali, gujarati, marathi, among others, are prevalent, researchers observed the limitations of models confined to english proficiency @xcite . this prompted the development of a system capable of processing code-mixed languages for sentiment analysis. a significant hurdle in this endeavor has been the scarcity of labeled datasets. notably, a few manually annotated datasets for offensive language and hate speech detection in tamil @xcite , @xcite , and kannada @xcite have been released, marking crucial contributions to the field. the study @xcite aimed to assess the relationship between online hate speech against migrants and refugees and social acceptance in spain. using intergroup contact and mediated intergroup contact theory, the research sought to validate hate speech as an indicator of social acceptance across spanish provinces. analyzing 97,710 tweets and secondary public data on migration, the study found no significant correlation between hate speech, foreign population proportions, and citizen attitudes toward immigrants. despite fluctuations in hate speech presence from 2015 to 2020, no clear negative correlation emerged between foreign population proportions and hate speech on twitter. similarly, the anticipated negative correlation between attitudes toward migration and hate speech on twitter could not be statistically confirmed. the paper @xcite outlines the development of a novel twitter corpus comprising roughly 6,000 tweets annotated for hate speech targeting immigrants. this corpus aimed to serve as a reference dataset for monitoring hate speech through automated systems. the annotation scheme was meticulously crafted to encompass various factors influencing hate speech, resulting in a tagset beyond hate speech alone, including aggressiveness, offensiveness, irony, stereotype, and experimental intensity categories. while discussing the annotated data, the study focuses on hate speech intensity and its interrelation with stereotype, aggressiveness, and offensiveness. the findings indicate nuanced trends, showcasing implicit incitement in most hateful tweets. stereotype prevalence is notably high in lower intensity degrees, indicating its role in implicit incitement. the study @xcite achieved notable success in hate speech detection within code-mixed tamil-english tweets using a synonym-based bi-lstm model. with an f1 score of 0.8169, the bi-lstm model outperformed other models evaluated, demonstrating its effectiveness in distinguishing hate and non-hate texts. specifically, in classifying hate speech, the model attained an f1 score of 0.8110, while for non-hate texts, it achieved an f1 score of 0.8050 this study (basava and karri, 2021) tackles the pervasive issue of hate speech proliferation across social media platforms by introducing an ensemble system utilizing transformer models. specifically, it aims to identify offensive language within codemixed posts/comments in dravidian languages (malayalam-english and tamil-english). situated within the framework of the hate speech and offensive content identification in dravidian-codemix (hasoc) @xcite initiative, this research emphasizes the rising impact of hate speech online and the urgent need for robust detection methods. the ensemble method showcased promising performance during development, notably achieving scores of 0.93 for tamil and 0.80 for malayalam, utilizing the model hsu_transemb. however, when assessed on the test set, the performance declined, registering 0.66 for tamil with the muril model and 0.73 for malayalam using hsu_transemb, indicating the necessity for more comprehensive datasets to enhance model robustness and efficacy in tackling hate speech in multilingual social media settings. (jayanthi and gupta, 2021) applied transformerbased models, utilizing a cased version of multilingual bert and xlm-roberta. employing bert at the sentence level, they transformed subword-level representations into word-level representations by averaging sub-token representations for improved classification. this innovative fusion architecture integrated a bidirectional lstm model to capture diverse word patterns, enhancing classification accuracy, and resulting in a 79.67% accuracy in classifying tamil tweets. ## methodology convolutional neural networks (cnns) excel in text classification tasks by utilizing convolutional and pooling layers to extract hierarchical features from sequential data, such as text @xcite . these networks employ convolutional filters of varying sizes to detect n-gram features within the input text, followed by pooling layers that condense and aggregate the extracted features. by learning local relationships between words and capturing essential patterns, cnns effectively discern hate speech or offensive language within textual data. their ability to model intricate relationships within text makes cnns a potent tool in the realm of hate speech detection. ## conclusion this research delves into the realm of hate speech detection in tamil, with a particular emphasis on themes related to migration and refuge within the framework of the caste/migration hate speech detection shared task. leveraging a convolutional neural network (cnn), our model exhibited a commendable f1 score of 0.76, demonstrating its efficacy in identifying hate speech amidst inherent complexities. the analysis sheds light on the competitive landscape, uncovering diverse performances across teams with scores ranging from 0.38 to 0.82. these variations underscore the challenges inherent in addressing hate speech nuances in the tamil language. as part of our future endeavors, we intend to enhance our approach by expanding our dataset and incorporating transformer models, aiming to further improve the accuracy of hate speech detection in this linguistic context.