IR lab@ IITV at S em E val-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using SVM (2020)

Abstract: This paper describes the IRlab@IIT-BHU system for the OffensEval 2020. We take the SVM with TF-IDF features to identify and categorize hate speech and offensive language in social media for two languages. In subtask A, we used a linear SVM classifier to detect abusive content in tweets, achieving a macro F_1 score of 0.779 and 0.718 for Arabic and Greek, respectively.

## introduction nowadays, the internet is accessed by half of the world population 1 . people from different cultures and educational backgrounds interact on social media. they express their opinions, dissent and even hatred against any individual, or a group using some hate and offensive language. but use of hate and offensive language not only lowers the morale of individuals, but may cause mental agony and trauma. so automatic identification of offensive languages is need of the hour. almost all social media have policies against use of abusive language, but identification is a challenge. it is not possible to monitor manually or using a static set of rules. hate speech and offensive language belongs to natural language and therefore nlp techniques can be tried to search for offensive content from textual data. the enthusiasm to explore these sub-fields is to possibly limit the offensive language and hate speech on user-generated content, particularly on social media. two popular social media platforms for researchers to study are twitter and facebook, a social network website where people "tweet" and "message" short posts. we mainly focus on detecting whether a tweet or message contains offensive content or not (subtask a). in this paper, a machine learning technique is used to identify the offensive language. using the arabic dataset annotated by @xcite and greek dataset annotated by @xcite , we train our support vector machine (svm) classifier using term frequency-inverse document frequency(tf-idf) as features. the main aim of this paper is to establish a baseline for this two languages (arabic and greek) with traditional machine learning approach. the rest of this paper is organized as follows. in section 2, we briefly outline some previous work on offensive language identification. the dataset description, features extraction, and model description are presented in section 3. results are presented in section 4, followed by conclusion and future work in section 5. ## related work many organizations from academia and industry regularly host workshops and seminars such as ta-cos foot_1 , abusive language online(alw) foot_2 , trac foot_3 by @xcite . recently two surveys by schmitdt and @xcite and @xcite show how to tag an offensive tweet from features like sentiment, linguistic features, different lexical resources. task 6 of semeval 2019 by @xcite was one such effort which helps to solve the three subtasks mentioned in sec 1.1 for twitter data (only english language). most of the previous works are based on english, but some work also reported in other languages like arabic by @xcite , german by @xcite , slovene by @xcite ) and chinese by @xcite . @xcite created a set of obscene words as a seeding list called seedwords (sw) and computed log odds ratio (lor) for each word unigram and bigram. like semeval, germeval was organized by @xcite . not only monolingual, but some attempted a more challenging case of code mixed language like hinglish by @xcite . hateval foot_4 also organised to detect hate speech in twitter. in this competition, they fixed two specific different targets, immigrants and women in both languages, spanish and english. hasoc foot_5 is a shared task on hate speech and offensive content identification in indo-european languages(english, hindi, and german). ## data and methodology the subtasks in the shared task are different from each other. in sub-task a, the aim is to identify offensive tweets. sub-task a requires sensitivity to meticulous changes in the meaning of the word, while sub-task b is more pronounced. however, both suffer from data sparsity. the earlier work on hate speech and offensive content identification depended on feature engineering. the handmade feature extraction requires knowledge about language, which is a difficult task for a person who is new to the language. our classification method depends upon tf-idf features. figure 1 shows the methodology of the paper. ## results we trained a linear svm model on the training set and tested them on the validation set, using scikit-learn 9 . the experimental results were based on tf-idf features. the result of the classifier on validation data is shown in ## conclusion and future work in this study, we report our systems for offenseval sub-task a. we trained a linear svm classifier with a tf-idf feature model to detect offensive tweets and messages. the evaluation result indicates that our system is capable of detecting offensive language and provides a good potential of identifying targets. however, there is room for improvement. in the future, to capture the subtle meaning and to overcome data sparsity, we plan to investigate a combination of selected surface features and pre-trained word embedding.