MUCS @ D ravidian L ang T ech-2024: Role of Learning Approaches in Strengthening Hate-Alert Systems for code-mixed text (2024)

Abstract: Hate and offensive language detection is the task of detecting hate and/or offensive content targetting a person or a group of people. Despite many efforts to detect hate and offensive content on social media platforms, the problem remains unsolved till date due to the ever growing social media users and their creativity to create and spread hate and offensive content. To address the automatic detection of hate and offensive content on social media platforms, this paper describes the learning models submitted by our team - MUCS to “Hate and Offensive Language Detection in Telugu Codemixed Text (HOLD-Telugu): DravidianLangTech@EACL” - a shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024 invites the research community to address the challenges of detecting hate and offensive language in Telugu language. In this paper, we - team MUCS, describe the learning models submitted to the above mentioned shared task. Three models: Three models: i) LR model - a Machine Learning (ML) algorithm fed with TF-IDF of n-grams of subword, word and char_wb are in the range (1, 3), (1, 3), and (1, 5), ii) TL- a pretrained BERT models which makes use of Hate-speech-CNERG/bert-base-uncased-hatexplain model and iii) Ensemble model which is the combination of ML classifieres( MNB, LR, GNB) trained CountVectorizer with word and char ngrams of range (1, 3) and (1, 5) respectively. Proposed LR model trained with TF-IDF of subword, word and char n-grams outperformed the other models with macro F1 scores of 0.6501 securing 15th rankin the shared task for Telugu text.

## introduction twitter, facebook, linkedin, instagram, and other social media platforms have become popular places for people to spend their time and communicate with each other @xcite . while social media platforms offers numerous benefits, it also comes with drawbacks, including the spread of harmful content such as hate speech, offensive, abusive, and fake news content. hate speech refers to any type of communication that targets, disparages, or encourages violence against an individual or group of people @xcite . disseminating hateful content about a group or a community has a detrimental effect on those who are targeted by it. these victims experience stress, depression, and other mental health issues, and in extreme circumstances, they might even commit suicide @xcite . therefore, it is necessary to detect hof content to maintain healthy online platforms. usually hof content on social media is written by mixing words or sub-words belonging to more than one language known as code-mixed text. the code-mixed nature of hof content is challenging because of its linguistic diversity @xcite . the rest of the paper is organized as follows: while section 2 describes the literature on hof language identification in social media text, sec-tion 3 focuses on the description of the models submitted to the shared task followed by the experiments and results in section 4. conclusion and future works are included in section 5. 2 related work hof content detection in code-mixed text is a growing area of study and several researchers have contributed to this area. some of the related works for detecting hof language are described below: to identify hof content in malayalam and tamil code-mixed texts, @xcite presented ml models (support vector classifier (svc), mnb, lr, and random forest (rf)) trained with tf-idf of character and word sequences in the range (1, 5) and (1, 2) respectively for malayalam code-mixed text, and tf-idf of character and word sequences in the range (1, 7) and (1, 4) respectively for tamil code-mixed text. they also trained ml models concatenating character and word tf-idf. among their proposed models, svc models outperformed other models obtaining macro f1 scores of 0.74 and 0.86 for malayalam and tamil codemixed texts respectively. bhawal et al. (2021) from the above literature, it is found that there are several techniques for detecting hof content in code-mixed text. however, there are only few studies that focus on telugu code-mixed text indicating the need for further research and innovation in this field. ## conclusion and future work this paper describes the models submitted by our team -mucs, to "hate and offensive language detection in telugu codemixed text (hold-telugu)" shared task at dravidian-langtech@eacl 2024, to identify hof content in code-mixed telugu text. three models: i) lr model -a ml algorithm trained with tf-idf of character and word sequences in the range (1, 5) and (1, 3) respectively and sub-words, ii) ensemble model -a combination of ml classifiers (mnb, lr, and gnb) trained with countvectorizer of character and word sequences in the range (1, 5) and (1, 3) respectively, iii) hateexplain_tl -a model based on tl approach with a bert variant, are submitted to the shared task for detecting hof content in telugu code-mixed text. the proposed lr model outperformed the other models with a macro f1 score of 0.65 for telugu code-mixed text. effective feature extraction techniques and classifiers will be explored further.