Dong Rongsheng, Li Libing, Zhang Qingpeng, Cai Guoyong
1Guangxi Key Laboratory of Trusted SoftwareGuilin University of Electronic TechnologyGuilin541004China.
2Mintaian Insurance Surveyors & Loss Adjusters Group Company Ltd.Shenzhen518000China.
IEEE Trans Comput Soc Syst. 2018 Jan 11;5(1):265-276. doi: 10.1109/TCSS.2017.2786545. eCollection 2018 Mar.
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.
社交媒体分析已经得出了关于人类活动模式的新的定量见解。社交媒体分析的许多应用，从大流行预测到地震响应，都需要深入了解当人类遇到不熟悉的情况时这些模式是如何变化的。在本文中，我们选择中国的两次地震作为中国最大的微博网站新浪微博（简称微博）中的社会背景。在提出一个形式化的微博信息流模型来表示微博上的信息传播之后，我们从三个主要角度研究信息传播：个体特征、互动参与者之间的社会关系类型以及真实互动网络的拓扑结构。定量分析得出以下结论。首先，邓巴数的影子在“宣称的朋友/关注者”分布中很明显，并且每个参与者的也参与了地震信息传播的朋友/关注者数量呈现典型的幂律分布，这表明了富者愈富的现象。其次，一个人的关注者数量是用户影响力的最关键因素。陌生人是地震后传播实时新闻的非常重要的力量。第三，两种类型的真实互动网络都具有无标度和小世界特性，但组织结构较为松散。此外，不同影响力群体之间的相关性表明，与其他在线社交媒体相比，微博上的讨论主要由认证用户主导和影响。
