Pardeep at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Deep Learning

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 727–734 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 727 Pardeep at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media Using Deep Learning Pardeep Singh School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi-110067 pardeepsinghinfo@gmail.com Satish Chand School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi-110067 schand20@gmail.com Abstract The rise of social media has made informa- tion exchange faster and easier among the peo- ple. However, in recent times, the use of of- fensive language has seen an upsurge in so- cial media. The main challenge for a service provider is to correctly identify such offensive posts and take necessary action to monitor and control their spread. In this work, we try to ad- dress this problem by using sophisticated deep learning techniques like LSTM, Bidirectional LSTM and Bidirectional GRU. Our proposed approach solves 3 different Sub-tasks provided in the SemEval-2019 task 6 which incorpo- rates identification of offensive tweets as well as their categorization. We obtain significantly better results in the leader-board for Sub-task B and decent results for Sub-task A and Sub- task C validating the fact that the proposed models can be used for automating the offen- sive post-detection task in social media. 1 Introduction Social media has revolutionized the way of com- munication among the people. It is an instant communication medium which connects people all over the world and shares their views. But, some people misuse this freedom by using the of- fensive language through posts or comments to de- fame, insult or target an individual or a group of individuals. The mainstream media have reported various cases of suicide and depression due to trolling and cyberbullying in social media. Hence it becomes worrisome for the corporates, govern- ment organizations and security agencies to ei- ther stop or mitigate this type of behavior of the users. Manually it is impossible to check the neg- ative behavior of users due to the volume, veloc- ity and variety of data coming from the social net- works. Hence there is an utmost need to develop a system which automatically identifies and catego- rizes the offensive language in social networks. To tackle these issues SemEval-2019 (Zampieri et al., 2019b) aimed exactly at that need and organized a task in identifying and categorizing offensive lan- guage in social media. This task is divided into three Sub-tasks. Sub-task A - Offensive language identification. Sub-task B - Categorization of offense types. Sub-task C - Offense target identification. All the three Sub-tasks are related to each other. In Sub-task A, we have to identify whether a given set of tweets is offensive or not. It is a binary clas- sification task based on tweet text. In Sub-task B, the main challenge is to categorize the tweets which are offensive in Sub-task A into targeted or untargeted. Sub-task C is comparatively challeng- ing than other two Sub-tasks due to the multi-class nature. Its …

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