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

TL;DR

This study uses machine learning and social network analysis to quantitatively examine the presence, influence, and discussion topics of over 20,000 Anonymous Twitter accounts from 2008 to 2019, challenging prior qualitative claims.

Contribution

It introduces the first large-scale computational analysis of Anonymous on Twitter, identifying influential accounts and analyzing their discussion topics with novel machine learning methods.

Findings

Identification of highly influential accounts within the network

Similarity in discussion topics among key influencer accounts

Quantitative evidence supporting previous qualitative claims about the group

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 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.