Towards Safe Social Surfing: Effective Adult Account Detection in Twitter
Hanqiang Cheng , McGill SOCS

Abstract:

Over the past few years, Twitter has emerged as an increasingly influential platform for real-time information distribution and discovery. However, it has been taking a bit of dark turn into adult content, which could significantly hinder the further popularity of Twitter and/or cause serious legal issues. Existing techniques for adult content detection are ill-suited for detecting adult accounts in Twitter.

To tackle this problem, we propose iterative social based classification (ISC)}, an effective solution for adult account detection for online social networks. ISC consisting of three key components: (1) collective interest, a novel social link based feature for effective discrimination of adult accounts; (2) a tag based label propagation algorithm which explores the tagged content embedded in tweets to further boost the detection accuracy; and (3) a social based linear classifier that integrates social consistency based on the collective interest feature and maximal tag scores computed from tag based label propagation. Evaluations using large-scale real-world Twitter data demonstrate that our ISC solution significantly outperforms existing methods in detecting adult accounts. It is able to identify adult accounts accurately among $1.07$ million accounts using only $100$ labeled adult accounts.