IIITDWD _ SVC @ D ravidian L ang T ech-2024: Breaking Language Barriers; Hate Speech Detection in T elugu- E nglish Code-Mixed Text (2024)

Abstract: Social media platforms have become increasingly popular and are utilized for a wide range of purposes, including product promotion, news sharing, accomplishment sharing, and much more. However, it is also employed for defamatory speech, intimidation, and the propagation of untruths about particular groups of people. Further, hateful and offensive posts spread quickly and often have a negative impact on people; it is important to identify and remove them from social media platforms as soon as possible. Over the past few years, research on hate speech detection and offensive content has grown in popularity. One of the many difficulties in identifying hate speech on social media platforms is the use of code-mixed language. The majority of people who use social media typically share their messages in languages with mixed codes, like Telugu–English. To encourage research in this direction, the organizers of DravidianLangTech@EACL-2024 conducted a shared task to identify hateful content in Telugu-English code-mixed text. Our team participated in this shared task, employing three different models: Xlm-Roberta, BERT, and Hate-BERT. In particular, our BERT-based model secured the 14th rank in the competition with a macro F1 score of 0.65.

## introduction in contemporary society, social media plays a pivotal role in the daily lives of many individuals. text messages across various platforms hold considerable influence, both positively and negatively. on a positive note, social media serves as a global connector, fostering creativity, enhancing skills, and providing entertainment. additionally, it facilitates the swift dissemination of breaking news. conversely, the prevalence of hate speech and the dissemination of inaccurate information about individuals, groups, or societies represent undesirable phenomena. social media platforms are regrettably exploited for expressing destructive views and eliciting negative emotions through hate and fraudulent communications. in the present era, there is a high degree of trust in social media, so misinformation propagated by media outlets or influential figures is often accepted as true. consequently, individuals disseminate false information using inappropriate language, with hashtags like #hatespeech gaining prominence on platforms such as twitter and youtube, particularly during the emergencies like covid-19 pandemic. furthermore, some individuals erroneously believe that engaging in abusive language or hate speech can confer fame and notoriety. social media platforms are actively striving to eradicate such negative textual content, recognizing the severe consequences it can have on individuals' lives. while social media users are increasingly cognizant of the issue, exposure to hate news persists, even when the true story is known. efforts to address this problem involve the development of machine learning and deep learning models capable of identifying hate speech in text data @xcite . given the language's global prevalence, numerous models have been trained on english data @xcite . however, it is imperative to acknowledge that hate speech extends beyond english, with regional languages being utilized for its propagation. telugu, a dravidian language spoken in andhra pradesh and telangana, india, is one such language. motivated by this realization, dravidian-langtech@eacl-2024 initiated a shared task for the classification of hate and non-hate speech detection in tenglish (telugu-english) code-mixed dataset @xcite . our team participated in the shared task; we employed various techniques, including transliteration, and translation during preprocessing. in addition, we subsequently utilized three distinct models for embedding extraction, hatebert, xlm-roberta, and bert, and secured 14th position with a f1-score of 0.6565 for bert-based (cased) among all competing teams. the article is structured as follows: section 2 presents the background study. the details of the dataset and methodology are presented in section 3, and finally, the results are discussed in section 4. at last, in section. 5 concluded and talked about future research direction. we have written some ethics in section 6 for the work which had done. ## related work the exploration of hate speech detection in dravidian code-mixed text remains a relatively under explored topic, as most previous research has predominantly focused on high-resource languages such as english. however, recent attention from the research community has been directed towards hate speech detection in dravidian code-mixed text data @xcite . the gold standard corpus for detecting hate speech in three dravidian languages: tamil (chakravarthi et al., 2020), malayalam (chakravarthi et al.), and kannada @xcite was developed by @xcite . this corpus was established as part of a shared task, stimulating active engagement from multiple teams. the majority of these teams concentrated on leveraging knowledge derived from pre-trained transformer models to address the challenges associated with low-resource languages. @xcite @xcite for instance, utilized a cross-lingual pre-trained model like mbert in conjunction with support vector machines (svm) to identify hate speech in tanglish and manglish text. furthermore, @xcite adopted an ensemble setup, combining machine learning (ml) and deep learning (dl) based models to effectively detect transphobic content in tamil and malayalam text. however, telugu, being one of the major dravidian languages widely spoken in telangana and andhra pradesh, has been relatively less explored in this context. this marks the first attempt to identify hate content in telugu-english code-mixed text. comprehensive dataset consisting of 4000 and 500 comments in the train and test stages respectively. these comments were collected from youtube, as stated by the organizers @xcite . the main objective of this task is to classify each telugu-english code-mixed social media comment at the sentence level, determining whether it falls into the hate or non-hate categories. our team actively participated in this task and achieved an impressive rank of 14 th position. for more detailed information about this dataset, plese refer to preprocessing raw data is essential in optimizing it for compatibility with machine learning models. even a small adequate data preprocessing improves a model's efficiency significantly. the different kinds of data preprocessing methods we used as illustarted in figure 1 . ## result and discussion the proposed model was tested on three distinct embedding representations generated using pretrained encoder-based models. the comparative results between these models are presented in in contrast, xlm-roberta slightly lags behind, likely due to the high-resource english text. our team presented the results of the bestperforming model, bert, in the competition. the organizers of the shared task evaluated model performance using the macro f1 score, and our team secured the 14 th rank among the participating teams. ## conclusion and future research direction the study provides the working notes of the model presented during the dravidianlangtech-2024 shared task. the experimental findings suggest that the performance of the model can be enhanced by translating the original code-mixed text and leveraging the knowledge derived from monolingual pre-trained models. additionally, this work can be extended to incorporate the fine-tuning of language models using domain-specific data.