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.
Highlights
With the development of Internet technologies, social media platforms are increasingly being favored by users owing to their unique advantages of timeliness, arbitrariness, and concealment
By applying the supernetwork theory, a new Internet water army detection model is established from the theoretical perspective
According to previous practice [21], [40], [51], the performance of the Internet water army detection model was evaluated based on four metrics, namely Accuracy, Precision, Recall, and F1-score, which are obtained through a confusion matrix
Summary
With the development of Internet technologies, social media platforms are increasingly being favored by users owing to their unique advantages of timeliness, arbitrariness, and concealment. With respect to the present paper, studies focusing on the application of the supernetwork theory in network public opinion area shed light on the understanding of online information analysis, especially for the information within the Internet communication platforms (see the works of Ma and Liu [12]; Tian and Liu [13]; Liu et al [14]; Wang et al [15]) Based on these valuable investigations and achievements, we can better and more comprehensively identify the features of Internet water armies, and proposing a more effective detection model.
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