HUB @ D ravidian L ang T ech- EACL 2021: Identify and Classify Offensive Text in Multilingual Code Mixing in Social Media (2021)

Abstract: This paper introduces the system description of the HUB team participating in DravidianLangTech - EACL2021: Offensive Language Identification in Dravidian Languages. The theme of this shared task is the detection of offensive content in social media. Among the known tasks related to offensive speech detection, this is the first task to detect offensive comments posted in social media comments in the Dravidian language. The task organizer team provided us with the code-mixing task data set mainly composed of three different languages: Malayalam, Kannada, and Tamil. The tasks on the code mixed data in these three different languages can be seen as three different comment/post-level classification tasks. The task on the Malayalam data set is a five-category classification task, and the Kannada and Tamil language data sets are two six-category classification tasks. Based on our analysis of the task description and task data set, we chose to use the multilingual BERT model to complete this task. In this paper, we will discuss our fine-tuning methods, models, experiments, and results.

## introduction and background social media platforms are playing an increasingly important role in people's modern social life. even applications in academic exchanges and technological dissemination are becoming more and more popular @xcite . in recent years, various offensive comments directed at individuals, groups, races, and countries that have appeared in social media have attracted attention in academic and industrial fields @xcite . even more worrying is that offensive comments/posts spread very quickly on social media @xcite . as far as the current situation is concerned, the covid-19 virus is spreading and raging on a global scale. the work of @xcite showed us the age distribution of users who used controversial terms on social media during the covid-19 virus epidemic, with the total share of the 18-24 and 25-34 age groups being 49% @xcite . combining the work of depoux and others, we can realize that what is more terrifying than the speed and harm of the virus is the public panic caused by rumors and hostile comments on social media @xcite . what makes us feel encouraged is that similar issues that are currently appearing on social media have been highly valued in the academic and industrial fields (ahmad and murad, 2020). however, the current automation technology is mostly applied to some languages with a large number of users (such as english, spanish user groups, etc.) (zahiri and ahmadvand, 2020; @xcite . the dravidian languages were first documented in tamili (tamil-brahmi) script engraved on cave walls in tamil nadu's madurai and tirunelveli districts in the 6nd century bce. according to the 14th century sanskrit text lilatilakam, which is a grammar of manipravalam, the spoken languages of modernday kerala and tamil nadu were identical, and they were referred to as "dramia" (tamil). malayalam split from tamil after 16th century. the earliest known inscriptions in sanskrit are from the 1st century bce, such as the ayodhya inscription of dhana and ghosundi-hathibada. sanskrit borrowed many words and grammatical structure from tamil, pali and prakrit. tamil languages is one of the longest-surviving classical languages in the world which is older than any surviving language in india. for tamil, there few works on sentiment analysis @xcite . combined with our analysis of the negative impact of negative posts/comments in social media, we believe that it is very meaningful and necessary to study the task of offensive language identification in dravidian languages-eacl 2021 foot_0 @xcite . the dataset in the task is the corpus obtained by chakravarthi and others from social media comments/posts. use this corpus to create the dravidian languages code-mixed dataset for tamil-english, malayalam-english, and kannada-english @xcite . code-mixing is mixing of two or more languages in the conversation @xcite @xcite . what we need to accomplish is to design an automated system and enter a youtube comment into the system. then let the system automatically detect which of the categories not-offensive, offensive-untargeted, offensivetargeted-individual, offensive-targeted-group, offensive-targeted-other, or not-in-indentedlanguage the comment content should be classified into. there is usually a lot of noise in the text of social media comments. also, the data set for this task is text with code mixed types. combining these characteristics, we chose to use the pre-trained language model multilingual bert that achieved excellent results in tasks in the natural language processing field to complete this task @xcite . regarding the problem of code-mixing language, we try to quote tf-idf to alleviate the adverse effect of code-mixing on the result. in the next part 3 and part 4, we will introduce our methods and experiments in the task in detail. ## related work recently, various issues arising on online social media platforms on the internet have been receiving attention from many parties. mossie et al. showed us that they use deep learning methods to process some posts on social media. their purpose is to predict the target groups that may be subjected to hate attacks. this work provides a very valuable reference for relevant governments and organizations to formulate measures to protect vulnerable groups @xcite . williams et al. use data science methods to analyze the connection between data from crime, census, and twitter, revealing to us that hate crime in the digital age is a complete process, not a discrete event (williams et al., 2020). velásquez et al. used mathematical analysis and modeling methods to detect and evaluate malicious content related to covid-19 on online social media. obtained the critical point of the virus spreading at multiple levels @xcite . vidgen et al. used machine learning methods to perform a quantitative analysis of whether islamophobic hate speech in social media may be strong or weak @xcite . the appearance of a large number of fake news on social media has induced hostile comments to a certain extent. to detect fake news in social networks, zhou et al. proposed a theory-driven fake news detection model. this method investigates fake news from multiple levels, involving lexical semantics, social psychology, and supervised learning method models. the contribution of this work is not only to increase the recognition rate of fake news but also to enhance the interpretability of fake news. what is more worthy of our attention is that the method of zhou et al. can also detect bad news early when the content information is limited @xcite . ## conclusion on the three different language data sets provided by the task organizer, we combined the tf-idf algorithm and the output of the multilingual bert model and introduced the cnn block as a shared layer. experimental results prove that our conjecture is feasible, but our method needs to be improved. especially to eliminate the over-fitting phenomenon in the training phase. when we rechecked our work, we also discovered our omissions. on the kannada and tami data sets, the verification set and the test set are different in the setting of the maximum sentence length. at the same time, our model has many areas that can be improved. for example, the use of classifiers and the replacement of cnn blocks are areas that we can try to optimize in future work. stop words and some special symbols can be deleted in data preprocessing. try to use data enhancement and other methods on the problem of data imbalance. we also hope that our system and methods can give other teams that pay attention to such tasks some inspiration and reference. in future work, we will not only improve our methods and systems but also continue to pay attention to related code-mixing fields progress.