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The hacktivist group Anonymous is unusual in its public-facing nature. Unlikeother cybercriminal groups, which rely on secrecy and privacy for protection,Anonymous is prevalent on the social media site, Twitter. In this paper were-examine some key findings reported in previous small-scale qualitativestudies of the group using a large-scale computational analysis of Anonymous'presence on Twitter. We specifically refer to reports which reject the group'sclaims of leaderlessness, and indicate a fracturing of the group after thearrests of prominent members in 2011-2013. In our research, we present thefirst attempts to use machine learning to identify and analyse the presence ofa network of over 20,000 Anonymous accounts spanning from 2008-2019 on theTwitter platform. In turn, this research utilises social network analysis (SNA)and centrality measures to examine the distribution of influence within thislarge network, identifying the presence of a small number of highly influentialaccounts. Moreover, we present the first study of tweets from some of theidentified key influencer accounts and, through the use of topic modelling,demonstrate a similarity in overarching subjects of discussion between theseprominent accounts. These findings provide robust, quantitative evidence tosupport the claims of smaller-scale, qualitative studies of the Anonymouscollective.

Behind the Mask: A Computational Study of Anonymous' Presence on Twitter