Title :
Detecting Spam in Chinese Microblogs - A Study on Sina Weibo
Author :
Lin Liu ; Kun Jia
Author_Institution :
Sch. of Comput. Sci., Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
Sina Weibo is the most popular and fast growing microblogging social network in China. However, more and more spam messages are also emerging on Sina Weibo. How to detect these spam is essential for the social network security. While most previous studies attempt to detect the microblogging spam by identifying spammers, in this paper, we want to exam whether we can detect the spam by each single Weibo message, because we notice that more and more spam Weibos are posted by normal users or even popular verified users. We propose a Weibo spam detection method based on machine learning algorithm. In addition, different from most existing microblogging spam detection methods which are based on English microblogs, our method is designed to deal with the features of Chinese microblogs. Our extensive empirical study shows the effectiveness of our approach.
Keywords :
computer network security; learning (artificial intelligence); social networking (online); unsolicited e-mail; Chinese microblog; Sina Weibo; Weibo spam detection method; machine learning algorithm; microblogging social network; microblogging spam detection; social network security; spam message detection; spammer identification; Feature extraction; Logistics; Machine learning; Machine learning algorithms; Support vector machines; Twitter; Vectors; data mining; machine learning; spam detection; web security;
Conference_Titel :
Computational Intelligence and Security (CIS), 2012 Eighth International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4673-4725-9
DOI :
10.1109/CIS.2012.135