A bhi P aw@ D ravidian L ang T ech: Abusive Comment Detection in T amil and T elugu using Logistic Regression (2023)

Abstract: Abusive comments in online platforms have become a significant concern, necessitating the development of effective detection systems. However, limited work has been done in low resource languages, including Dravidian languages. This paper addresses this gap by focusing on abusive comment detection in a dataset containing Tamil, Tamil-English and Telugu-English code-mixed comments. Our methodology involves logistic regression and explores suitable embeddings to enhance the performance of the detection model. Through rigorous experimentation, we identify the most effective combination of logistic regression and embeddings. The results demonstrate the performance of our proposed model, which contributes to the development of robust abusive comment detection systems in low resource language settings. Keywords: Abusive comment detection, Dravidian languages, logistic regression, embeddings, low resource languages, code-mixed dataset.

## introduction the widespread prevalence of abusive comments in online social networks (osns) has raised serious concerns about the safety and well-being of users. detecting and mitigating abusive content has become a paramount objective in fostering a respectful and inclusive online environment. while significant progress has been made in abusive comment detection, much of the existing research primarily focuses on high-resource languages, leaving a critical gap in addressing this issue in low resource languages. the lack of resources and research devoted to comprehending and addressing abusive content in these languages poses a hindrance to the development of efficient detection systems. in recent studies conducted by abusive comment detection in tamil -acl 2022 @xcite , a shared task was introduced to detect categories of abusive comments in social media, focusing on languages such as tamil and code-mixed language containing tamil and english scripts. swaminathan et al. @xcite proposed a classification model that combines language-agnostic sentence embeddings with tf-idf vector representation, employing traditional classifiers. balouchzahi et al. @xcite addressed abusive comment detection in native tamil script texts and code-mixed tamil texts using n-gram-multilayer perceptron (n-gram-mlp) and 1d convolutional long short-term memory (1d conv-lstm) models. s.n. et al. @xcite employed tf-idf with char-wb analyzers and the support vector machine (svm) classifier with a polynomial kernel for classification. these studies demonstrate initial attempts to tackle abusive comment detection in low resource languages, but further research is necessary to address the challenges specific to these languages comprehensively. the unique linguistic characteristics and nuances of dravidian languages present significant challenges in detecting abusive comments accurately. additionally, the limited availability of annotated datasets and language-specific linguistic features further exacerbate these challenges. therefore, it is crucial to explore novel approaches and methodologies tailored specifically for detecting abusive comments in dravidian and other low resource languages. in this paper, we aim to bridge the research gap by comprehensively reviewing the state-of-the-art techniques and methodologies employed in abusive comment detection. our primary focus will be on addressing the challenges faced in low resource languages, with a specific emphasis on dravidian languages. the outcomes of this research have significant implications, as they contribute to building safer online communities in low resource language contexts, empower content moderators, and facilitate the development of automated systems capable of detecting and mitigating abusive comments effectively. by addressing the specific needs of dravidian languages, we pave the way for further research in other low resource languages, promoting a more inclusive and equitable digital space. in summary, this paper serves as a significant step towards understanding and combating abusive comment detection in low resource languages, with a particular focus on dravidian languages. by leveraging innovative techniques and proposing tailored solutions, we aim to make substantial progress in creating a safer and more respectful online environment for users of diverse linguistic backgrounds. this work was submitted as a part of the dravidi-anlangtech workshop, 2023 @xcite . ## related work numerous studies have been conducted to identify abusive comments in various languages; however, there has been relatively less work done in low resource languages, highlighting a research gap in this area. @xcite , @xcite conducted a shared task (at acl 2022) that aims at detecting the categories of abusive comments that are posted on social media. they aggregate the comments from social media in two languages, namely, tamil and in code mixed language containing tamil and english scripts. @xcite approached by building a classification model which includes different methods of feature extraction and the use of traditional classifiers. they propose a novel method of combining language-agnostic sentence embeddings with the tf-idf vector representation that uses a curated corpus of words as vocabulary, to create a custom embedding, which is then passed to an svm classifier. @xcite addresses the abusive comment detection in native tamil script texts and code-mixed tamil texts. to address this challenge, two models: i) n-gram-multilayer perceptron (n-gram-mlp) model utilizing mlp classifier fed with char-n gram features and ii) 1d convolutional long short-term memory (1d conv-lstm) model, were submitted. @xcite used tf-idf with char-wb analyzers with random kitchen sink (rks) algorithm to create feature vectors and the support vector machine (svm) classifier with polynomial kernel for classification. transformer is an attention-based technique that effectively captures the contextual connections between words (or subwords) within a text, attracting significant attention in the field. @xcite sbow a knowledge integration strategy that combines sentence embeddings from bert, roberta, fasttext and a subset of languageindependent linguistic features. @xcite present xlm-roberta and deberta models for two multi-class text classification tasks in tamil. @xcite used a pretrained transformer model such as indic-bert for feature extraction, and on top of that, svm classifier is used for stance detection. @xcite presented an exploration of different techniques which can be used to tackle and increase the accuracy of models using data augmentation in nlp. @xcite used pre-trained transformer models such as bert,m-bert, and xlnet. certain works experiment with multiple methods individually to find the best performing model. @xcite approached the task with three methodologies -machine learning, deep learning and transformer-based modeling. for machine learning, eight algorithms were implemented, among which random forest gave the best result with tamil+english dataset for deep learning, bi-directional lstm gave best result with pretrained word embeddings. in transformer-based modeling, they used indicbert and mbert with fine-tuning, among which mbert gave the best result. @xcite employed three machine learning (lr, dt, svm), two deep learning (cnn+bilstm, cnn+bilstm with fasttext) and a transformer-based model (indic-bert). the experimental results show that logistic regression (lr) and cnn+bilstm models outperformed the others. @xcite experimented with logistic regression, svms, gradient boost classifier, finetuned muril; with gradient boost classifier emerged to be the best performing method. @xcite also used three methods to optimize their results: ensemble models, recurrent neural networks, and transformers. ## method the objective of this task is to determine if a given youtube comment exhibits abusive content. ## conclusion by transforming textual data into numerical features and employing the logistic function for multiclass classification, logistic regression enables accurate identification and categorization of abusive comments into multiple abusive categories, facilitating comprehensive analysis and understanding of the data. as future work we look forward to use fine tuning on the specific datasets and investigate crosslingual transfer learning.