Abstract:

In order to detect spammers more effectively in Weibo，an approach based on multi-view fusion was proposed． First，a user representation strategy for integrating multi-view information was designed to characterize users from 3 views，namely，user behavior，social relationship and text content． In view of the deficiencies that the existing approaches do not fully consider the user＇s fans and user＇s environment in social networks，new features such as fan ratio，fan average bidirectional connection rate，community-based bidirectional connection rate，communitybased cluster coefficient，etc． were introduced． Then，a multi-view fusion decision model based on a linear weighting function was constructed． A linear weighting fusion was carried out based on the classification results from each view． The optimal fusion coefficient was obtained by minimizing the approximate error，and then the final classification result was obtained． The test result on the real data from Weibo show that this approach can not only effectively detect spammers，with significant improvement in precision and F1-sorce，but also exhibits greater stability especially when processing unbalanced data． It also analyzes the impact of different views on the final detection effect， and the results show that the user＇s social relationship view has the most significant effect.

Key words: Weibo, spammer detection, linear weighting function, multi-view fusion