Detecting spam in Twitter Network

Now a days Twitter has become one of the biggest social networking sites around. Twitter has some useful features which make it especially helpful for research on almost any topic under the sun. This is because Twitter pages are viewable to anyone, even those without accounts, and the site has a search function which pulls in all recent posts dealing with a given topic or phrase. Twitter is a micro blogging service where users can post 140 character messages called tweets. Unlike Facebook and MySpace, Twitter is directed, meaning that a user can follow another user, but the second user is not required to follow back. Most accounts are public and can be followed without requiring the owner’s approval. With this structure, spammers can easily follow legitimate users as well as other spammers.

As online social networking sites become more and more popular, they have also attracted the attentions of the spammers. In the article of First Monday, “detecting spam in twitter network” by Yardi and others, they mentioned that Twitter, a popular micro-blogging service, is studied as an example of spam bots detection in online social networking sites. A machine learning approach is proposed to distinguish the spam bots from normal ones. To facilitate the spam bots detection, three graph-based features, such as the number of friends and the number of followers, are extracted to explore the unique follower and friend relationships among users on Twitter.

Unfortunately, spam is becoming an increasing problem on Twitter as other online social network sites. Spammers use Twitter as a tool to post multiple duplicate updates containing malicious links, abuse the reply function to post unsolicited messages to users, and hijack trending topics.