Spams classification and their diffusibility prediction on Twitter through sentiment and topic models

Abstract

ABSTRACT Alike web spam has been a major issue to almost every aspect of the current World Wide Web, social spam has led to a serious threat to the utilities of online social media, particularly in information diffusion. To combat this challenge, the significance and impact of social media and its content should be analyzed critically. To address this issue, Twitter is used as a case study, where we have modeled the contents of information through topic models and coupled it with the user-oriented features to predict spam diffusion with a good accuracy. Latent Dirichlet Allocation (LDA), a widely used topic modeling technique is applied to capture the latent topics from the tweets’ documents. The major contribution of this work is twofold. Firstly, analyzing the variation of sentiment and topics in spam/non-spam information, and secondly, constructing a feature set that classifies tweets more accurately into spam and non-spam category as compared to the existing approaches. The extensive simulation reveals the variation in sentiments and topics shared by the spam and non-spam tweets.