Citation

Dong R, Li L, Zhang Q, Cai G. IEEE Trans. Comput. Soc. Syst. 2018; 5(1): 265-276.

Affiliation

1Guangxi Key Laboratory of Trusted SoftwareGuilin University of Electronic TechnologyGuilin541004China.

Copyright

(Copyright © 2018, Institute of Electrical and Electronics Engineers, Inc.)

DOI

10.1109/TCSS.2017.2786545

PMID

32391405

PMCID

PMC7176041

Abstract

Social media analytics has drawn new quantitative insights of human activity patterns. Many applications of social media analytics, from pandemic prediction to earthquake response, require an in-depth understanding of how these patterns change when human encounter unfamiliar conditions. In this paper, we select two earthquakes in China as the social context in Sina-Weibo (or Weibo for short), the largest Chinese microblog site. After proposing a formalized Weibo information flow model to represent the information spread on Weibo, we study the information spread from three main perspectives: individual characteristics, the types of social relationships between interactive participants, and the topology of real interaction networks. The quantitative analyses draw the following conclusions. First, the shadow of Dunbar's number is evident in the "declared friends/followers" distributions, and the number of each participant's friends/followers who also participated in the earthquake information dissemination show the typical power-law distribution, indicating a rich-gets-richer phenomenon. Second, an individual's number of followers is the most critical factor in user influence. Strangers are very important forces for disseminating real-time news after an earthquake. Third, two types of real interaction networks share the scale-free and small-world property, but with a looser organizational structure. In addition, correlations between different influence groups indicate that when compared with other online social media, the discussion on Weibo is mainly dominated and influenced by verified users.

Language: en

Keywords

Emergency response; Sina-Weibo; online collective behavior; social network analysis