IRlab@IITV at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using SVM

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 2012–2016 Barcelona, Spain (Online), December 12, 2020. 2012 IRlab@IIT-BHU at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media using SVM Anita Saroj, Supriya Chanda, Sukomal Pal Department of Computer Science and Engineering IIT(BHU), Varanasi, INDIA {anitas.rs.cse16, supriyachanda.rs.cse18, spal.cse}@itbhu.ac.in 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 F1 score of 0.779 and 0.718 for Arabic and Greek, respectively. 1 Introduction Nowadays, the internet is accessed by half of the world population1. 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 Mubarak et al.,(2020) and Greek dataset annotated by Pitenis et al.,(2020), 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. 1.1 SemEval Sub-Task According to Zampieri et al.,(2020) the aim of SemEval2020 is to build a system able to detect offensive content. For the first time in SemEval2019, three subtasks were proposed by Zampieri et al.,(2019). We only participate in Sub-task A for Arabic and Greek languages. Some tweet examples from training dataset are listed in Table 2 and Table 3 respectively. This work is licensed under a Creative Commons Attribution 4.0 International License. License …

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