Overview of Abusive Comment Detection in T amil- ACL 2022 (2022)

Abstract: The social media is one of the significantdigital platforms that create a huge im-pact in peoples of all levels. The commentsposted on social media is powerful enoughto even change the political and businessscenarios in very few hours. They alsotend to attack a particular individual ora group of individuals. This shared taskaims at detecting the abusive comments in-volving, Homophobia, Misandry, Counter-speech, Misogyny, Xenophobia, Transpho-bic. The hope speech is also identified. Adataset collected from social media taggedwith the above said categories in Tamiland Tamil-English code-mixed languagesare given to the participants. The par-ticipants used different machine learningand deep learning algorithms. This paperpresents the overview of this task compris-ing the dataset details and results of theparticipants.

## introduction their distribution of digital information has increased to a greater extent. the importance of the online social networks (osns) has grown significantly in recent years, and they have become a go-to source for acquiring news, information, and entertainment @xcite @xcite . however, despite many positive impacts of employing osns, a growing body of evidence indicates that there is an ever-increasing number of malevolent actors who are exploiting these networks to spread poison and cause harm to other individuals @xcite . the term "hate speech" (hs) refers to any form of communication that is abusive, insulting, intimidating, and/or incites violence or discrimination and that disparages an individual or a vulnerable group on the basis of characteristics such as ethnicity, gender, sexual orientation, or religious affiliation @xcite @xcite @xcite . because of this diversity in thematic foci, we refer to them as themes. examples of topics include misogyny, sexism, racism, transphobia, homophobia, and xenophobia @xcite @xcite . the abusive comments targeting people have a huge impact on them psychologically @xcite . this task lays a foundation on how these comments can be detected for dravidian language tamil. tamil is a dravidian classical language used by the tamil people of south asia. tamil is an official language of tamil nadu, sri lanka, singapore, and the union territory of puducherry in india. significant minority speak tamil in the four other south indian states of kerala, karnataka, andhra pradesh, and telangana, as well as the union territory of the andaman and nicobar islands @xcite @xcite . it is also spoken by the tamil diaspora, which may be found in malaysia, myanmar, south africa, the united kingdom, the united states, canada, australia, and mauritius. tamil is also the native language of sri lankan moors @xcite @xcite @xcite . tamil, one of the 22 scheduled languages in the indian constitution, was the first to be designated as a classical language of india. tamil is one of the world's longest-surviving classical languages. the earliest epigraphic documents discovered on rock edicts and "hero stones" date from the 6th century bc. tamil has the oldest ancient non-sanskritic indian literature of any indian language @xcite . since the comments posted online contain mixture of languages that are familiar with the users that are posting the comments, the task also considers detecting the comments from the tamil-english code mixed language. the goal of this task is to identify whether a given comment contains abusive comment. a comment/post within the corpus may contain more than one sentence but the average sentence length of the corpora is 1. the annotations in the corpus are made at a comment/post level. the participants were provided development, training and test dataset in tamil and tamil-english languages. the dataset is tagged using various classes namely, homophobia, misandry, counter-speech, misogyny, xenophobia, transphobic and hope speech. to the best of our knowledge, this is the first shared task on abusive detection in tamil at this fine-grained level. 11 teams participated for detecting abusive comments in tamil language and tamil-english language tasks. ## task description the task is primarily a comment/post-level classification task. given a youtube comment, the systems submitted by the participants should classify it abusive categories. the participants were provided with development, training and test dataset in tamil and tamil-english. the dataset is tagged using various classes namely, homophobia, misandry, counter-speech, misogyny, xenophobia, transphobic and hope speech. 10 teams participated for detecting abusive comment in tamil language and 11 teams participated for the tamil-english language. ## data description the tamil language training data contains 2240 comments, the validation set contains 560 comments, and the test data set includes 699 comments. the tamil-english language test data set contains 5943 comments, the validation set contains 1486 comments and the 1857 test comments. the distribution of the seven categories in the whole dataset is shown in the participants have predominantly used "transliteration" as one of the pre-processing strategies. the tamil-english code-mixed texts necessitate this approach. apart from transliteration, removal of punctuation, stop words have also been used. class balancing of the data has also been attempted as the distribution of the class labels in the given training dataset. ## error analysis of the systems the participants have used the standard metrics such as weighted precision, weighted recall, and weighted f-score to evaluate the performance of their systems. the equations of these metrics are given below. ## conclusion this shared task aims at detecting the categories of abusive comments that are posted on social media. this kind of analysis would quantify the negativity that is spread in the society, which in turn should be turned into positivity either by enacting laws to enforce restrictions on posting comments on social media. this has been the motivation behind hosting this shared task which has attempted to aggregate the comments from social media in two languages, namely, tamil and in code mixed language containing tamil and english scripts. these comments were trained by various machine learning, deep learning, and transfer learning models. 11 teams participated in tamil and tamil-english languages tasks. 7 categories of abusive categories were tagged in the collected comments. the ranking of the teams was done based on the performance shown by the systems that were used by the participants and the in-depth analysis done by them. it was observed that the transformer models showed better performance when compared to that of the rest of the systems.