Spam Detection on Twitter Using Traditional Classifiers
Social networking sites have become very popular in recent years. Users use them to find new friends, updates their existing friends with their latest thoughts and activities. Among these sites, Twitter is the fastest growing site. Its popularity also attracts many spammers to infiltrate legitimate users' accounts with a large amount of spam messages. In this paper, the authors discuss some user-based and content-based features that are different between spammers and legitimate users. Then, they use these features to facilitate spam detection. Using the API methods provided by Twitter, they crawled active Twitter users, their followers/following information and their most recent 100 tweets.