Machine Learning Techniques applied to Twitter Spammers Detection

Description

Every minute more than 320 new accounts are created on Twitter and more than 98,000 tweets are posted. Among the multitude of Twitter users, spammers and cybercriminals aim to pervade and strike legitimate users' accounts with a large amount of troublesome messages. Hence, the Social Network propagation opens new modalities for cyber-crime perpetration, while the spamming phenomenon exploits specific mechanism of messaging process. This research shows that Machine Learning (ML) may provide a powerful tool to support spammer detection in Twitter. The present paper compares the performance of three different ML algorithm in tackling this task. The experimental session involves a publicly available dataset.

Additional details