Using naive bayes to detect spammy names in social networks

Abstract

Many social networks are predicated on the assumption that a member's online information reflects his or her real identity. In such networks, members who fill their name fields with fictitious identities, company names, phone numbers, or just gibberish are violating the terms of service, polluting search results, and degrading the value of the site to real members. Finding and removing these accounts on the basis of their spammy names can both improve the site experience for real members and prevent further abusive activity. In this paper we describe a set of features that can be used by a Naive Bayes classifier to find accounts whose names do not represent real people. The model can detect both automated and human abusers and can be used at registration time, before other signals such as social graph or clickstream history are present. We use member data from LinkedIn to train and validate our model and to choose parameters. Our best-scoring model achieves AUC 0.85 on a sequestered test set. We ran the algorithm on live LinkedIn data for one month in parallel with our previous name scoring algorithm based on regular expressions. The false positive rate of our new algorithm (3.3%) was less than half that of the previous algorithm (7.0%). When the algorithm is run on email usernames as well as user-entered first and last names, it provides an effective way to catch not only bad human actors but also bots that have poor name and email generation algorithms.