ISC: An Iterative Social Based Classifier for Adult Account Detection on Twitter

Hanqiang Cheng, Xinyu Xing, Xue Liu, Qin Lv

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The widespread of adult content on online social networks (e.g., Twitter) is becoming an emerging yet critical problem. An automatic method to identify accounts spreading sexually explicit content (i.e., adult account) is of significant values in protecting children and improving user experiences. Traditional adult content detection techniques are ill-suited for detecting adult accounts on Twitter due to the diversity and dynamics in Twitter content. In this paper, we formulate the adult account detection as a graph based classification problem and demonstrate our detection method on Twitter by using social links between Twitter accounts and entities in tweets. As adult Twitter accounts are mostly connected with normal accounts and post many normal entities, which makes the graph full of noisy links, existing graph based classification techniques cannot work well on such a graph. To address this problem, we propose an iterative social based classifier (ISC), a novel graph based classification technique resistant to the noisy links. Evaluations using large-scale real-world Twitter data show that, by labeling a small number of popular Twitter accounts, ISC can achieve satisfactory performance in adult account detection, significantly outperforming existing techniques.

Original languageEnglish (US)
Article number6895278
Pages (from-to)1045-1056
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Volume27
Issue number4
DOIs
StatePublished - Apr 1 2015

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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