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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 X after the acquisition of Elon Musk), we demonstrated that irregular following patterns are prevalent and are indicative of automated fake accounts. Notably, we found that these detected groups of anomalous followers exhibited consistent behavior across multiple accounts. 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