ARGUABLY at C om MA @ ICON : Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned I ndic BERT (2021)

Abstract: The proliferation in Social Networking has increased offensive language, aggression, and hate-speech detection, which has drawn the focus of the NLP community. However, people’s difference in perception makes it difficult to distinguish between acceptable content and aggressive/hateful content, thus making it harder to create an automated system. In this paper, we propose multi-class classification techniques to identify aggressive and offensive language used online. Two main approaches have been developed for the classification of data into aggressive, gender-biased, and communally charged. The first approach is an ensemble-based model comprising of XG-Boost, LightGBM, and Naive Bayes applied on vectorized English data. The data used was obtained using an Indic Transliteration on the original data comprising of Meitei, Bangla, Hindi, and English language. The second approach is a BERT-based architecture used to detect misogyny and aggression. The proposed model employs IndicBERT Embeddings to define contextual understanding. The results of the models are validated on the ComMA v 0.2 dataset.

## introduction a burgeon in social networking has been seen in the past few years. the number of platforms and users has increased by 77% from 2014 to 2021. social media, due to its easy accessibility and freedom of use, has transformed our communities and how we communicate. one of the widespread impacts can be seen through trolling, cyberbullying, or sharing aggressive, hateful, misogynistic content vocalized through platforms like facebook, twitter, and youtube. the intensity and hostility lying in aggressive words, abusive language, or hate speech is a matter of grave concern. these are used to harm the victim's status, mental health, or prestige @xcite . this articu-lation of hatefulness often travels from the online to the offline domain, resulting in organized riotlike situations and unfortunate casualties, which causes disharmony in society. hence, it has become crucial for scholars and researchers to take the initiative and find methods to identify the source and articulation of aggression. aggression is a feeling of anger or antipathy that results in hostile or violent behavior and readiness to attack or confront. according to @xcite , one can express aggression in a direct, explicit manner (overtly aggressive) or in an indirect, sarcastic way (covertly aggressive). hate speech can be used to attack a person or a group of people based on their color, gender, race, sexual orientation, ethnicity, nationality, religion @xcite . misogyny or sexism is a subset of hate speech ' @xcite and targets the victim based on gender or sexuality @xcite . while it is essential to identify hate speech in social networks, it is rather time-consuming to perform manually, considering the massive amount of data at hand. thus, there is a need to build an automated system for the identification of such aggression. however, distinguishing between acceptable content and hateful content is challenging due to the subjectivity of definitions and varying perceptions of the same content by different people, thus making it tedious to build an automated ai system. regardless, numerous studies exist that have explored different aspects of hateful and aggressive language and their computational modeling and automatic detection, such as toxic comments. to this end, several workshops such as 'abusive language online' (alw) @xcite , 'trolling, aggression and cyberbullying' (trac) @xcite , and semantic evaluation (semeval) shared task on identifying offensive language in social media (offenseval) @xcite have been organized. this paper presents our system for shared task on "multilingual gender biased and communal language identification @ icon 2021" @xcite . two approaches have been implemented developed for the classification of data into aggressive, gender biased, or communally charged. ## related work recently there has been an increase in the studies exploring different aspects of hate speech, sexism detection, aggressive language, and their computational modeling and automatic detection, such as trolling @xcite @xcite , racism @xcite @xcite , online aggression @xcite , cyberbullying @xcite , hate speech @xcite @xcite @xcite @xcite , and abusive language @xcite @xcite . the prevalent misogynistic and sexist comments, posts, or tweets on social media platforms have also come into light. @xcite analyzed sexist tweets and categorized them as hostile, benevolent, or other. @xcite provided an in-depth analysis of sexist tweets and further categorized them based on the type of harassment. @xcite performed linguistic analysis to detect misogyny and sexism in tweets. prior studies have explored aggressive and hateful language on platforms like twitter @xcite @xcite . using twitter data, @xcite proposed a supervised approach to categorize the text into racist and non-racist labels to detect anti-black hate speech on social media platforms. @xcite used an ensemble-based classifier to capture the grammatical dependencies between words in twitter data to anticipate the increasing cyberhate behavior using statistical approaches. @xcite curated a corpus of user comments for abusive language detection and applied machine learning-based techniques to identify subtle hate speech. @xcite used convolutional layers on word vectors to detect hate speech. @xcite provided the largest dataset on sexism categorization and applied a bert based neural architecture with distributional and word level embeddings to perform the classification task. bert based approaches also have become prevalent recently @xcite @xcite . there have also been an increasing number of shared tasks on agression indentification. @xcite aimed to identify aggressive tweets in social media posts in hindi and english datasets. (samghabadi et al., 2018) used lexical and semantic features and logistic regression for the hindi and english facebook datasets. (orasan, 2018) used machine learning methods such as svm and random forest on word embeddings for aggressive language identification. (raiyani et al., 2018) used fully connected layers on highly pre-processed data. (aroyehun and gelbukh, 2018)aroyehun and gelbukh (2018) used data augmentation and deep learning for aggression identification. ## task description the shared task focuses on the multi-label classification to identify the different aspects of aggression and offensive language usage on social media platforms. we have been provided with a multilingual, comma v 0.2 @xcite dataset consisting of 12,000 samples for training and an overall 3,000 samples for testing in four indian languages meitei, bangla, hindi, and english. we were required to classify each sample into one of the following labels: aggressive, gender biased, and communally charged. ## methodology 4.1 data preparation to get better accuracy, we require a dataset in english language. therefore, the multilingual input dataset have been passed through the spacylangdetect toolkit 1 . this toolkit consists of a pipeline for custom language detection. the sentence is categorized into the language it belongs to, i.e., hindi, bangla, or english, depending upon the probability assigned to that sentence. the sentences belonging to the hindi language were given the label "hi," those belonging to bangla were given the label "ba," and sentences in english were given the label "en." all the sentences belonging to the "hi" and "ba" labels were transliterated, the process of transferring a word from the alphabet of one language to another, to provide us with a uniform multilingual dataset in english. we must note that the labeling done is based on the language it is written in (as shown in example 3 figure 1 ) rather than the language itself (as shown in example 1 figure 1 ), which indicates that if the words used are those of english, irrespective of the language, it will be given the label "en". such sentences do not require transliteration. this data thus prepared has been used in both the proposed architectures as discussed below. ## conclusion the paper describes our experimentation over comma v 0.2 dataset consisting of multilingual, bangla, hindi, and english data to perform analysis on aggression, communal bias, and gender bias. we have proposed two strategies boosted voting ensemble and indicbert fine-tuned in this paper. the boosting voting ensemble outperforms indicbert in terms of instance f1 scores that showcase the robustness of our proposed approach as well its capabilities in handling all three labels efficiently. however, it should also be noted that indicbert majorly outperforms the ensemble approach in the individual task, highlighting its power in understanding contextual meanings related to aggression, communal bias, and gender bias. the f1 scores for aggression are relatively on the lower side because of the contextual overlaps between the output labels, which was not the case in gender and communal bias. in the future, the inclusion of better embeddings like glove and bert which capture the underlying semantic and lexical relations could improve the performance of the methodology manifolds. the application of ensembling techniques in a deep learning setting could be another set of experimentations to be considered.