Abstract: 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 past small-scale qualitative studies of the group via a large-scale computational analysis of Anonymous 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 key 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 influence within this large network, thus helping to provide a computational perspective on the findings of smaller-scale, more qualitative studies. Moreover, we present the first study of tweets from some of the identified `key' influencer accounts, through the use of topic modelling, finding a similarity in overarching topics of discussion between these influential accounts. Findings which further support the claims of smaller-scale, qualitative studies of the Anonymous collective.