Behind the Mask: A Computational Study of Anonymous' Presence on Twitter
This provides robust quantitative evidence for researchers studying hacktivist groups, though it is incremental as it builds on prior small-scale studies.
The study tackled the problem of understanding Anonymous' presence on Twitter by conducting a large-scale computational analysis of over 20,000 accounts from 2008-2019, using machine learning, social network analysis, and topic modeling to identify influential accounts and show similarity in discussion topics, supporting previous qualitative findings.
The hacktivist group Anonymous is unusual in its public-facing nature. Unlike other cybercriminal groups, which rely on secrecy and privacy for protection, Anonymous is prevalent on the social media site, Twitter. In this paper we re-examine some key findings reported in previous small-scale qualitative studies of the group using a large-scale computational analysis of Anonymous' presence on Twitter. We specifically refer to reports which reject the group's claims of leaderlessness, and indicate a fracturing of the group after the arrests of prominent members in 2011-2013. In our research, we present the first attempts to use machine learning to identify and analyse the presence of a network of over 20,000 Anonymous accounts spanning from 2008-2019 on the Twitter platform. In turn, this research utilises social network analysis (SNA) and centrality measures to examine the distribution of influence within this large network, identifying the presence of a small number of highly influential accounts. Moreover, we present the first study of tweets from some of the identified key influencer accounts and, through the use of topic modelling, demonstrate a similarity in overarching subjects of discussion between these prominent accounts. These findings provide robust, quantitative evidence to support the claims of smaller-scale, qualitative studies of the Anonymous collective.