Efficient spam detection across Online Social Networks

Abstract

Online Social Networks (OSNs) have become more and more popular in the whole world. People share their personal activities, views and opinions among different OSNs. At the same time, social spam appears more frequently and in various formats throughout popular OSNs. Therefore, efficient detection of spam has become an important and popular problem. This paper focuses on spam detection across multiple online social networks by leveraging the knowledge of detecting similar spam within a social network and using it in different networks. We chose Facebook and Twitter for our study targets, considering that they share the most similar features in posts, topics, and user activities, etc. We collected two datasets from them and performed analysis based on our proposed methodology. The results show that detection combined with spam in Facebook show a more than 50% decrease of spam tweets in Twitter, and detection combined with spam of Twitter shows a nearly 71.2% decrease of spam posts in Facebook. This means similar spam of one social network can greatly facilitate spam detection in other social networks. We proposed a new perspective of spam detection in OSNs.