Spam2Vec: Learning Biased Embeddings for Spam Detection in Twitter

Abstract

In this paper, we propose a semi-supervised framework Spam2Vec to identify spammers in Twitter. This algorithmic framework learns the spam representations of the node in the network by leveraging biased random walks. Our spammer detection method yields an AUC of 0.54 with precision@100 as 0.12 and performs significantly better with 7.77% increase in AUC and a 2.4 times improvement on precision over the best performing baseline.