Detecting Offensive Content on Social Media During Anti-Lockdown Protests in Michigan

Hateful and offensive speech on online social media platforms has been exacerbated by the turbulent and chaotic circumstances brought on by the coronavirus pandemic. A particularly contentious issue was lockdown orders issued by state governments designed to keep citizens safe by controlling the spread of the virus. To compel the government to relax these orders and restore normalcy, antilockdown protests were organized in many states. The economic, ideological, political, and health concerns related to the lockdowns and the associated protests were debated vigorously on social media platforms, many times using offensive content. Detecting such insulting and humiliating content is especially important during tumultuous times, when tensions are high, because such expressions online can quickly precipitate violence in the physical world. This paper presents an approach to detect hateful and offensive content from Twitter feeds collected after anti-lockdown protests in Lansing, Michigan. These tweets were labeled using a comprehensive definition of what constitutes offensive content based on its potential to trigger and incite people. Linguistic and auxiliary features were extracted from these labeled tweets. These features were further processed through feature selection and dimensionality reduction techniques. The preprocessed feature set was used to train machine learning models, which detect offensive content with an accuracy of around 84%. Our approach demonstrates the feasibility of identifying and tagging offensive content in politically motivated situations, even when such speech is dominated by contextual and circumstantial information. It can thus be used to mitigate the damage caused by widespread dissemination of offensive content.