Deep learning for Antisocial Behaviour Analysis on Social Media

Social Media platforms have become an imperative source of information related to urban functions and social behaviour. Analysis of social media data can provide meaningful insights into the personality and behaviour traits of its users. Behaviour studies have been typically conducted using questionnaire and survey data. Since most of our activities have moved online, our behaviour on social media has become a proxy for our real-world behaviour. In this paper, we collected data from Swarm and Twitter, two widely used social media platforms, to detect user’s antisocial behaviour with a high degree of accuracy. The behaviour was then categorized into different classes of antisocial behaviour using cutting edge deep learning technology. Four different deep learning models and two different word embeddings were experimented with to achieve a final model accuracy of 99%. Visually enhanced interpretation of the classification process and the word clouds for some of the most commonly used antisocial terms are presented, along with model training and error analysis.