Twitter Bots in Cyber-Physical-Social Systems: Detection and Estimation Based on the SEIR Model

Abstract

Bots are now part of the social media landscape, and thus, a threat to cyber-physical-social systems (CPSSs). A better understanding of their characteristic behaviors and estimation of their impact on public opinion could help improve the algorithms to identify bots and help develop strategies to reduce their influence. The cosine function-based algorithm is able to compare the similarity between tweets and restore the course of information circulation. Combined with malicious features of an account, our method could effectively detect bots. We implement SEIR model to compute tweets with the hashtag #Huawei 5G and divide the trend propagation into the following four phases: formation, fermentation, explosion, and decay of trend. Sentiment analysis revealed the change of emotion and opinion among normal users in different stages and the manipulation attempt of bots behind it. Experiment results show that bots have very limited relation to users’ stance in whole. In early phase bots could affect those who are neutral. The influence of bots declines in later stage. Polarized views can hardly be changed.