An Internet Water Army Detection Supernetwork Model

Abstract

The emergence of Internet water armies has strongly affected the information quality of online communication platforms, thus disrupting the order of the Internet. Accurate detection of Internet water armies is therefore of great significance. Based on the supernetwork theory, a new Internet water army detection model is proposed in this paper, in which a supernetwork with four layers is established, including social subnetwork, information subnetwork, psychological subnetwork, and negative keyword subnetwork. Then, personal information of users, dissemination process of information, the transformation process of different psychologies, the similarity between different keywords, and the connections between different subnetworks are considered in the model. Thus, nine composite indexes are proposed, the majority of which are used for the first time in detecting Internet water armies. A dataset selected from the largest online communication platform in China, the Weibo website, is used to test the performance of the model. Four existing water army detection models introduced in previous studies are used to provide a comparison analysis. The results show that our proposed model has better performance in terms of accuracy and stability than the other four existing models, which thanks to the employment of the supernetwork theory. We believe that our proposed model could be helpful for information researchers to further understand the complex nature of Internet water armies, as well as for the government to better manage the Internet.

Keywords

• Internet water army detection
• supernetwork theory
• social media
• feature measurement
• machine learning classification