ENWalk: Learning Network Features for Spam Detection in Twitter

TL;DR

This paper introduces ENWalk, a novel framework that learns user features from Twitter social graphs to effectively detect spammers by capturing their unique content posting dynamics and social relationships.

Contribution

ENWalk is the first approach to incorporate spam-specific biased random walks for learning user representations in Twitter spam detection.

Findings

ENWalk outperforms existing spam detection methods.

Identifies two distinct spammer types and their social strategies.

Reveals new insights into spammer content posting dynamics.

Abstract

Social medias are increasing their influence with the vast public information leading to their active use for marketing by the companies and organizations. Such marketing promotions are difficult to identify unlike the traditional medias like TV and newspaper. So, it is very much important to identify the promoters in the social media. Although, there are active ongoing researches, existing approaches are far from solving the problem. To identify such imposters, it is very much important to understand their strategies of social circle creation and dynamics of content posting. Are there any specific spammer types? How successful are each types? We analyze these questions in the light of social relationships in Twitter. Our analyses discover two types of spammers and their relationships with the dynamics of content posts. Our results discover novel dynamics of spamming which are intuitive and arguable. We propose ENWalk, a framework to detect the spammers by learning the feature representations of the users in the social media. We learn the feature representations using the random walks biased on the spam dynamics. Experimental results on large-scale twitter network and the corresponding tweets show the effectiveness of our approach that outperforms the existing approaches