An Online Water Army Detection Method Based on Network Hot Events

With the advent of the mobile Internet era, large numbers of online water army active in the network are driven by interests. Spread network rumors and false information against network incidents, seriously disturbing the normal network order and affecting social harmony and stability. We analyzed the rumor publishing, dissemination and trace back process in social platforms. This paper proposes a query algorithm for similarity of text content. Based on the principle of rumor propagation, emotion analysis of texts and the characteristics of network public opinions, this paper studies the evolvement of network hot events over time, and proposes a set of detection methods based on current network events. The results show that the analysis of the rumors of the Online water army can reveal the evolving trend of network events well. Our method can achieve an accuracy of 93% in the Sina Weibo, which is of considerable reference value for realizing the automatic discovery of network public opinion hot spots and follow-up intelligent tracking.