Twitter content-based spam filtering

Abstract

Twitter has become one of the most used social networks. And, as happens with every popular media, it is prone to misuse. In this context, spam in Twitter has emerged in the last years, becoming an important problem for the users. In the last years, several approaches have appeared that are able to determine whether an user is a spammer or not. However, these blacklisting systems cannot filter every spam message and a spammer may create another account and restart sending spam. In this paper, we propose a content-based approach to filter spam tweets. We have used the text in the tweet and machine learning and compression algorithms to filter those undesired tweets.