Using naive bayes to detect spammy names in social networks

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Practical Evasion of a Learning-Based Classifier: A Case Study (2014, background): “…also witnessed several innovative applications of machine learning for detection of various kinds of security violations, e.g., drive-by-downloads [3], [4], malicious web pages [5], [6], compromised accounts and fake identities in social networks [7], [8], unwanted P2P traffic [9] and many others.”
Finding and Characterizing Information Warfare Campaigns (2020, methodology): “At that time [99] presented the application of the Naive Bayes model on Character N-grams features of LinkedIn account names in order to identify spammy accounts (first and last name as provided by the account owner).”
Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network (2016, methodology): “NB is considered one of the most efficient and effective inductive learning algorithms in the field of machine learning (Zhang, 2004a), and has been used as an effective classifier in several studies on social media (Bora, Zaytsev, Chang, & Maheswaran, 2013; Freeman, 2013; Wang, 2010b).”