Temporal propagating network approach to long-term evolutionary process of public opinion

• Published in: International journal of modern physics. C, Computational physics, physical computation Vol. 32; no. 4; p. 2150048
• Main Authors: Cai, Shi-Min, Liu, Peng-Cheng, Huang, Ping
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.04.2021; World Scientific Publishing Co. Pte., Ltd
• Subjects: Evolution; Public opinion; Social networks; non-Markov; heterogeneous cascade size; Public opinion; power law
• ISSN: 0129-1831; 1793-6586
• DOI: 10.1142/S0129183121500480

Public opinion quickly generated and propagated on online social networks brings huge influences on society and state security. Previous studies mostly analyze its snapshot in a short-term time interval to predict and control the explosive size, but neglect its long-term evolutionary process. In this paper, based on the online social network of Sina Weibo, we trace nine public opinion events in the nearly two-year duration to comprehensively observe the long-term evolutionary processes and characterize the temporal dynamics and propagating networks. The long-term evolutionary processes of public opinion are constructed by quantitatively measuring forwarding sizes at a daily scale. We show their non-Markov temporal dynamics by autocorrelation analysis, which is verified by the heavy-tail interval time distribution of individual forwarding behaviors. Also, the temporally propagating networks are abstracted from individual forwarding behaviors to represent the microcosmic organization of forming public opinion. The topological analysis of aggregating propagating networks shows that the microcosmic organization is generally constructed by a giant connected component and amounts of small connected components with strongly heterogeneous cascade sizes, and the corresponding degree distributions obeys a power law which is shaped by the giant connected component. Furthermore, we compare the follower–followee (i.e. friendship) network with the propagating network to unveil their potential correlation, and find that at large scale they behave a similar connection pattern. This work first projects public opinion into a process-based model to study its temporal dynamics and helps us to better understand the underlying mechanics of forming public opinion.