Online Social Network Service (OSNS) lead the fashion on internet nowadays[43]. Hundreds of millions people are using Facebook, Twitter,... Show moreOnline Social Network Service (OSNS) lead the fashion on internet nowadays[43]. Hundreds of millions people are using Facebook, Twitter, MySpace and other similar OSNS all over the world[18]. In China, people use Sina Weibo, Tencent Weibo, Renren instead of Facebook and Twitter. With those miracle tools, people communicate with others far from them at real time. However, in the wrong hands, those virtual communicating services are vulnerable from being leveraged to spread harmful or unwelcome spam messages to large number of people instantly. Besides illegal advertisements, the even worse spam messages could mislead you to phishing websites, or malware downloading links. Your account may be compromised and be used by spammers to continue spreading the virus to your acquaintance. The fight with spammers has been over decades. Thousands of smart scholars has developed different strategies to auto filter most of the spam messages[10][12][18]. In E-mail system, the earliest platform leveraged by spammers and hackers, it is reported that 98% of the common spam e-mails could be identified[5]. But in OSNS, it is just the beginning of the fight. In this work, I present a further study on the behavior of spammers and spammer accounts in Sina Weibo, the most popular OSNS in China. From the data I collected, I learn the differences pattern between spammers and legitimate users and try to finally identify the spammers. I study a dataset of 220K user profile data and 2.1 million of their most recent posted tweets. My method could finally recognize 84.4% of the spammer account while the overall classification accuracy achieves 89.9%. Because this method does not rely on the content of messages but the structure and pattern of them, I believe this method should work well for other OSNS such as twitter as well. M.S. in Computer Science, July 2012 Show less
