Analyzing Offensive Content and Emotional Dynamics in Black Lives Matter Discourse on Twitter

Abstract

The Black Lives Matter (BLM) movement seeks to spread awareness and fight against social and racial injustice. In 2020, BLM-related discussions surged on social media after the death of George Floyd and the protests that followed. Previous works have qualitatively analyzed the scaling, dynamics, and topics of BLM discussions on social media. However, very few works have studied the offensive content, the emotions expressed, and the topics of offensive discussions in BLM-related discussions. In this measurement study, to examine offensive language and emotion, we conduct a largescale study of BLM discussions on Twitter. We first develop a classifier that uses sentiment representation to aid offensive language detection. We then develop an emotion classifier based on deep attention fusion with sentiment features to classify emotions. We further use topic modeling to analyze the topics of offensive tweets. Our analysis of over 20 million tweets revealed that offensive tweets peeked in the weeks following George Floyd’s death and rapidly decreased but remained stable. The analysis further revealed that negative emotions were the most expressed emotions. Offensive reply network analysis reveals that most offensive replies are unidirectional. Our contribution in this work is five-fold: (1) We identify offensive content during BLM protests; (2) we identify online emotions that were significant in the offensive and non-offensive content during the protests; (3) we assess the characteristics of users who replied offensively and those who are the recipients of offensive content; (4) we assess emotion dynamics across offenders and recipients; (5) we identify the hot topics that most drove the offensive content on Twitter. Our work offers important implications for content moderation and the conscious and unconscious attitudes towards the black/African American community.