Abusive content detection in transliterated B engali- E nglish social media corpus (2021)

Abstract: Abusive text detection in low-resource languages such as Bengali is a challenging task due to the inadequacy of resources and tools. The ubiquity of transliterated Bengali comments in social media makes the task even more involved as monolingual approaches cannot capture them. Unfortunately, no transliterated Bengali corpus is publicly available yet for abusive content analysis. Therefore, in this paper, we introduce an annotated Bengali corpus of 3000 transliterated Bengali comments categorized into two classes, abusive and non-abusive, 1500 comments for each. For baseline evaluations, we employ several supervised machine learning (ML) and deep learning-based classifiers. We find support vector machine (SVM) shows the highest efficacy for identifying abusive content. We make the annotated corpus freely available for the researcher to aid abusive content detection in Bengali social media data.

## introduction with the popularity of social media, nowadays, user-generated contents are available in many languages. in various social media platforms, such as review forums and social networking sites, users express their feelings, opinions, emotion, etc. due to the open nature of social media, the presence of abusive, offensive, and hateful comments is common there @xcite . abusive language refers to the usage of demeaning, insulting, vulgar, or profane expression to attack individuals or groups @xcite ; however, there exist inconsistencies in the definitions of abusive language in various literature @xcite . for example, @xcite considered hate speech as a kind of abusive language, while @xcite distinguished it from abusive speech. as the presence of abusive and hatred content inflicts a negative impact on society and individuals @xcite @xcite , it is important to identify them. while plenty of resources are available for abusive language detection in english @xcite , limited research has been performed on abusive content analysis in low resource bengali language. code-mixing (cm) is a natural phenomenon of embedding linguistic units such as phrases, words, or morphemes of one language into an utterance of another @xcite . transliteration can be considered as a special form of code-mixing where the phonetic transformations of the words from a source language to a target language is performed. the presence of code-mixing and transliterated bengali (i.e., bengali text using the latin alphabet) is a common phenomenon in bengali, as shown by the previous studies @xcite . the existing research on abusive content or hate speech detection in bengali mainly investigated text written in bengali @xcite @xcite @xcite . although a few works addressed the phenomenon of code-switching in word-level or sentence level (i.e., presence of both english and bengali words written using the alphabet of the corresponding language), most of them did not consider transliterated bengali. only @xcite utilized a small number of transliterated bengali comments (around 200 abusive comments) in their study. english is the second language in bangladesh, the country with the highest number of bengali native speakers; therefore, transliterated bengali is ubiquitous in bengali social media content. hence, to detect abusive content in bengali social media, it is essential to consider the transliterated bengali text. for example, "dor besorom mor tui" is an abusive comment which is written in transliterated bengali; the corresponding english translation is, " you are shameless, you die". the monolingual approaches can not identify it as an abusive comment as the transliterated words neither exist in the bengali or english dictionary nor available in the monolingual training data. supervised ml classifiers are more effective for abusive content detection than the word-list based approaches, as shown in previous studies @xcite . however, ml classifiers require annotated training data, which are missing for transliterated bengali text. therefore, in this work, we develop an annotated corpus for transliterated bengali for abusive content detection and make them publicly available foot_0 . we manually annotate around 3000 transliterated bengali comments collected from youtube into abusive and non-abusive categories, 1500 for each category. to the best of our knowledge, this is the largest annotated transliterated bengali corpus for abusive content analysis. we then employ popular ml classifiers, logistic regression (lr), support vector machine (svm), random forest (rf), and deep learning-based bidirectional long short-term memory (bilstm) architecture for baseline evaluations. ## related work researchers explored code-mixed and transliterated content for tasks like linguistic analysis, partof-speech (pos) tagging, and sentiment analysis in various south asian languages, such as hindi and bengali @xcite @xcite . @xcite introduced a twitter dataset for the classification of offensive tweets written in the hindi-english code-switched language. however, such a dataset for abusive content analysis in transliterated bengali is not available yet. ## corpus creation the developed corpus contains user-generated transliterated bengali text regarding several bengali dramas and celebrities (i.e., opinion data). ## results and discussion we report the precision (p abus ), recall (r abus ) and f1 scores (f 1 abus ) of various classifiers for identifying abusive comments. the t p , f p , and f n values of the abusive class are defined as follows-t @xmath0, p @xmath1 we perform 10-fold cross-validation on the transliterated corpus. we run each classifier 10 times and provide the range of r abus , p abus and f 1 abus scores. ## conclusion and future work identifying abusive content in social media is of paramount importance due to its detrimental impact. not addressing this problem can lead to the increasing growth of harassment and cyberbullying in social media. while there have been few works for abusive speech detection in bengali social media content, they mostly ignored the presence of transliterated bengali. in this paper, we present the most comprehensive study of abusive content detection in transliterated bengali text by providing an annotated corpus and baseline evaluations. our future works will focus on expanding the size of the transliterated corpus, providing more rigorous analysis, and introducing a customized deep learning model to improve the performance of abusive content detection in transliterated bengali text.