دانلود رایگان مقاله انگلیسی انتشار اطلاعات در رسانه های اجتماعی در بلایای طبیعی- نشریه IEEE

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 powerlaw 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.

IV. CONCLUSION

Weibo has been ubiquitously integrated into people’s everyday lives in China. Both Sina-Weibo and Tencent-Weibo had more than five million users in early May 2013, and their open platforms (including the data API service) have been improved constantly. Although the very existence of user records raises huge concerns of privacy, these user records also create a historic opportunity, that is offering for the first time unbiased data of unparalleled detail on the behavior of not one, but millions of individuals. In this paper, we performed a comprehensive analysis of Weibo information diffusion during earthquakes. We found that symbolic representation applied to the WIF model is indeed a feasible choice for the empirical study of human behavior based on online social media data sets. In retrospect, our primary inspiration came from the description mechanism of concepts and relationships in ontology theory. The main feature of this idea is that it can give a formal expression for the data structure and the analysis process (such as the extraction of FRCN and SRCN). However, the structure of social networks is only a starting point. When people talk about the “connectedness” of a social network, in general, they are really talking about two related issues. One is who is linked to whom; and the other is the fact that each individual’s actions have implicit consequences for the outcomes of everyone in the system [56]. In fact, Fig. 8 has given us some intuition that there is probably a serious structural imbalance between the “declared” relationship network and the real interaction network [57]–[59]. In addition, to measure public perceptions in emergencies, many researchers have worked extensively on the evolution of public opinion during information dissemination based on Twitter [60], which allows for many interesting directions for future work.