Out of the Shadows: Analyzing Anonymous’ Twitter Resurgence during the 2020 Black Lives Matter Protests
Keywords:Qualitative and quantitative studies of social media, Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, Psychological, personality-based and ethnographic studies of social media, Organizational and group behavior mediated by social media; interpersonal communication mediated by social media
AbstractUntil recently, there had been little notable activity from the once prominent hacktivist group, Anonymous. The group, responsible for activist-based cyber attacks on major businesses and governments, appeared to have fragmented after key members were arrested in 2013. In response to the major Black Lives Matter (BLM) protests that occurred after the murder of George Floyd, however, reports indicated that the group was back. To examine this apparent resurgence, we conduct a large-scale study of Anonymous affiliates on Twitter. We first use machine learning to identify a significant network of more than 33,000 Anonymous accounts and use topic modelling of tweets collected from these accounts to find evidence of sustained interest in topics related to BLM. We then use sentiment analysis on tweets focused on these topics, finding evidence of a united approach amongst the group, with positive tweets typically being used to express support towards BLM and negative tweets typically being used to criticize police actions. Finally, we examine the presence of automation in the network, identifying indications of bot-like behavior across the majority of Anonymous accounts. These findings show that whilst the group has seen a resurgence during the protests, bot activity may be responsible for exaggerating the extent of this resurgence.
How to Cite
Jones, K., Nurse, J. R., & Li, S. (2022). Out of the Shadows: Analyzing Anonymous’ Twitter Resurgence during the 2020 Black Lives Matter Protests. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 417-428. https://doi.org/10.1609/icwsm.v16i1.19303