Spam Detection and Spammer Behaviour Analysis in Twitter Using Content Based Filtering Approach

Abstract

Twitter is one of the most popular social media networks and therefore it is prone to misuse. One of the ways in which people misuse Twitter is by spamming. Spam becomes an issue once a communication medium especially one, which enables global communication and handle huge amount of online data. Since Twitter is popular among so many people, it makes it easy for spammers to thrive. Spammers are people who send unwanted messages to people to either advertise a product or lure the victims into clicking malicious links, which may affect their user systems. The main objective of these spammers is usually to make money from their victims. In the last years, several systems has-been made with the aim of determining whether a user is a spammer or not. However, these systems cannot filter each spam message and a different account can be created by a spammer and used to send other messages. This paper proposes a content-based approach, which can be used to filter spam tweets. The approach involves using tweets in machine learning and compression algorithms in order to filter the undesired tweets.