Aggression Detection on Social Media Text Using Deep Neural Networks

Vinay Singh, Aman Varshney, Syed Sarfaraz Akhtar, Deepanshu Vijay, Manish Shrivastava

Abstract: In the past few years, bully and aggressive posts on social media have grown significantly, causing serious consequences for victims/users of all demographics. Majority of the work in this field has been done for English only. In this paper, we introduce a deep learning based classification system for Facebook posts and comments of Hindi-English Code-Mixed text to detect the aggressive behaviour of/towards users. Our work focuses on text from users majorly in the Indian Subcontinent. The dataset that we used for our models is provided by TRAC-1in their shared task. Our classification model assigns each Facebook post/comment to one of the three predefined categories: “Overtly Aggressive”, “Covertly Aggressive” and “Non-Aggressive”. We experimented with 6 classification models and our CNN model on a 10 K-fold cross-validation gave the best result with the prediction accuracy of 73.2%.