Public Opinion Spamming
Ziyu Guo, Liqiang Wang, Yafang Wang, Guohua Zeng, Shijun Liu, Gerard de Melo · 2018
Microblogs serve hundreds of millions of active users, but have also attracted large numbers of spammers. While traditional spam often seeks to endorse specific products or services, nowadays there are increasingly also paid posters intent on promoting particular views on hot topics and influencing public opinion. In this work, we fill an important research gap by studying how to detect such opinion spammers and their micro-manipulation of public opinion. Our model is unsupervised and adopts a Bayesian framework to distinguish spammers from other classes of users. Experiments on a Sina Weibo hot topic dataset demonstrate the effectiveness of the proposed approach. A further diachronic analysis of the collected data demonstrates that public opinion spammers have developed sophisticated techniques and have seen success in subtly manipulating the public sentiment.