CIC at SemEval-2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in Twitter

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 382–386 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 382 CIC at SemEval-2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in Twitter Iqra Ameer, Muhammad Hammad Fahim Siddiqui, Grigori Sidorov, and Alexander Gelbukh Instituto Politécnico Nacional (IPN), Center for Computing Research (CIC), Av. Juan Dios Batiz, s/n, Zacatenco, 07738, Mexico City, Mexico {iqraameer133,hammad.fahim57}@gmail.com, {sidorov,gelbukh}@cic.ipn.mx Abstract In recent years, the use of social media has in- creased incredibly. Social media permits Inter-net users a friendly platform to express their views and opinions. Along with these nice and distinct communication chances, it also allows bad things like usage of hate speech. Online automatic hate speech detection in various aspects is a significant scientific problem. This paper presents the Insti- tuto Politécnico Nacional (Mexico) approach for the Semeval 2019 Task-5 [Hateval 2019] (Basile et al., 2019) competition for Multilingual Detec- tion of Hate Speech on Twitter. The goal of this paper is to detect (A) Hate speech against immi- grants and women, (B) Aggressive behavior and target classification, both for English and Spanish. In the proposed approach, we used a bag of words model with preprocessing (stemming and stop words removal). We submitted two different sys- tems with names: (i) CIC-1 and (ii) CIC-2 for Hat- eval 2019 shared task. We used TF values in the first system and TF-IDF for the second system. The first system, CIC-1 got 2nd rank in subtask B for both English and Spanish languages with EMR score of 0.568 for English and 0.675 for Spanish. The second system, CIC-2 was ranked 4th in subtask A and 1st in subtask B for Spanish language with a macro-F1 score of 0.727 and EMR score of 0.705 respectively. 1 Introduction The social media applications enable users to dis- cover, create and share contents handily, without specific expertise. This remarkably boosted the 1 http://www.pewinternet.org/2017/07/11/online-harass- ment-2017/ Last visited: 01/02/2019 amount of data generated by the users, within a pro- cess that some people call “democratization” of the web (Silva et al., 2016). Still, this liberty also per- mits for the publication of data, which is insulting and hurtful both regarding the ethics of democracy and the privileges of some categories of people - hate speech (HS). The Hate Speech (HS) term is defined in the literature as an expression “that is abusive, insulting, intimidating, harassing, and in- cites to violence, hatred, or discrimination. It is di- rected against people by their race, ethnic origin, religion, gender, age, physical condition, disability, sexual orientation, political conviction, and so forth.” (Erjavec and Kovacic, 2012). HS has turned into the main issue for each sort of online website, where user-produced content comes into sight: from the comments on any post to live chatting in online games. Such material can isolate users and inflame violence (Allan, 2013). Website operators as Facebook, Twitter, and gaming companies like Runic Games recognize that hateful data are …

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