  Automated social media accounts, known as bots, are increasingly recognized
as key tools for manipulative online activities. These activities can stem from
coordination among several accounts and these automated campaigns can
manipulate social network structure by following other accounts, amplifying
their content, and posting messages to spam online discourse. In this study, we
present a novel unsupervised detection method designed to target a specific
category of malicious accounts designed to manipulate user metrics such as
online popularity. Our framework identifies anomalous following patterns among
all the followers of a social media account. Through the analysis of a large
number of accounts on the Twitter platform (rebranded as Twitter after the
acquisition of Elon Musk), we demonstrate that irregular following patterns are
prevalent and are indicative of automated fake accounts. Notably, we find that
these detected groups of anomalous followers exhibit consistent behavior across
multiple accounts. This observation, combined with the computational efficiency
of our proposed approach, makes it a valuable tool for investigating
large-scale coordinated manipulation campaigns on social media platforms.
