Hope at SemEval-2019 Task 6: Mining social media language to discover offensive language

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 635–638 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 635 Abstract User’s content share through social media has reached huge proportions nowadays. However, along with the free expression of thoughts on social media, people risk getting exposed to various aggressive statements. In this paper, we present a system able to identify and classify offensive user-generated content. 1 Introduction With the constant spread of social media, users are spending increasing amounts of time on various social networking sites aiming to connect with peers, to share information or common interests. While users benefit from their use of social media by interacting with and learning from others, they are also at the risk of being exposed to large amounts of offensive contents. Considering that people are negatively affected by harmful contents, detecting online offensive language to protect users online safety becomes an urgent task. To address concerns on people’s access to offensive content over the internet, social media administrators often need to manually review online texts to detect and delete offensive materials. However, manually reviewing and identifying offensive messages is a highly human and time consuming task. Some automatic content filtering software packages have been developed to detect and filter offensive WebPages or paragraphs, mostly word-based approaches. The “OffensEval: Identifying and Categorizing Offensive Language in Social Media” task at the SemEval 2019 competition (Zampieri et al., 2019a) focuses on detecting and classifying offenses, pervasive in social media. In this paper, we present a system able to identify whether a tweet is abusive language or not, and if abusive, if it is offensive or not. We trained a model to differentiate between these categories and then analyzed the results to better understand how we can improve the system. The rest of the paper is organized as follows: section 2 presents other projects related to offensive language identification, section 3 presents the project’s data set and methods, section 4 presents the results we have obtained and a short analysis, followed by our last point represented by section 5 with the conclusions. 2 Related Work This topic has attracted significant attention in recent years, evidenced by increasing number of recent publications and a several scientific events such as ALW and TRAC workshops. Offensive language is often subdivided into various intercalated categories, since different subtasks have been grouped under this label. One of the most analyzed such language is “hate speech”, i.e. discriminative remarks, such as the racist or sexist ones (Norbata et al., 2016). Based on work on hate speech, cyberbullying and online abuse, Waseem et al., 2017 proposses a typology that captures central similarities and differences between subtasks and discuss its implications for data annotation and feature construction. Additionally, Waseem et al. (2017) emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest. Lexical detection methods for the offensive language tend to have low precision because they fail to classify messages …

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