Microblog, is a platform based on user relationships for sharing, transmitting and acquiring information, on which users can establish individual communities, update information with around 140 characters and achieve realtime sharing via WEB, WAP and a variety of clients . As the development of WEB2.0, microblog, which is a booming information communicating platform, develops rapidly. Since there are a large number of active users and hotspot information, the influence of microblog on information transmitting, the change of living habits, et al., cannot be ignored. While microblog users’ influence refers to as a user’s influence in the microblog community. The greater the influence is, the more attention the netizens pay to, and then impact on network will become remarkable. So microblog possesses vast potential for future development theoretically and practically, especially in the field of word of mouth marketing, information mining, public opinion controlling and so on.
At present, many scholars have started to pay attention to and study the microblog or twitter (in China, microblog is ordinarily called, so in the context “microblog” is used) all over the world. Also, the hot areas of these researches include the motivation and behaviors of microblog users, besides microblog social network structure. The evaluation of microblog users’ influence (microblog influence) has also become a new research focus in the analysis of the social network. Foreign studies mainly discuss Twitter, which is considered as the pioneer prototype of microblog. In addition, AKSHAYJAVA et al. (2007)  studied the data sets of Twitter from 1st April 2007 to 30th May 2007 and found that the main types of user intentions are: daily chatter, conversations, sharing information and reporting news. Then, they analyzed the microblog network on the growth, degree distribution, geographical distribution of Twitter users, and so on. Besides, TEUTTE (2010)  analyzed Twitter from network dynamics, including the description of the microblog network’s changes by the growth of in degree and out degree, network density, betweenness and so forth. KRISHNAMURTHY et al. (2008)  explored the structural characteristics of microblog network, and identified distinct classes of microblog users and their behaviors. Meanwhile, Chinese scholars mainly apply their minds to hotspot discovery in microblog network, propagation mechanism and user behavior characteristics. Caishuqin, Zhangjing (2012)  designed some metadata models for micro-blogging content through the structured metadata acquired from open APIs. And the hotspot discovery process was regarded as a value-added process of the original materials to clusters of hot products. Finally, a complete production and processing model was established.
However, there are a few academic researches on microblog users’ influence presently. GABRIEL W (1994)  made an evaluation of Twitter users’ influence learning from PageRank algorithm and considered the number of friends to be an important indicator of users’ influence. In other words, it means that the more the friends are, the bigger influence it has, and the more easily it has effects on others. Furthermore, the basic equations are consistent with PageRank algorithms. However, when taking into account the situation in China that a large number of microblog fans are traded, the model is not fully applicable. In fact, YUTO Y (2010)  proposed TU Rank (Twitter User Rank) based on User-Tweet Graph to rank the users, which laid a lot of emphasis on the quality of the content, while the influence of fans’ retreating was ignored. KLOUT , a famous assessment service on the influence of social network sites, uses the relationship among Facebook, LinkedIn and Twitter, and the user behavior (initiating a session, comments, forwarding, etc.) data. Assessing the users’ influence by Klout algorithm, Klout believes everyone has influence on the era of social media. Also, Klout measures your influence in the social networks, and give the insight into whom you do effect on and on what topics you are affected. Klout measures your influence index on a scale of 0 – 100. Kang (2011)  advanced a new algorithm to evaluate the influence of nodes in microblog social network through the users’ behavior and relationship based on the SINA microblog. They considered the frequency of posting microblogs as a factor to evaluate the users’ activity and presented Behavior-Relationship Rank algorithm after combining the users’ activity with PageRank. But they only took into account the frequency of posting microblogs as a factor to evaluate the users’ activity without referring to interaction behaviors such as the users’ mentioning friends, commenting, and forwarding microblogs etc., which also have effects on users’ influence.
In Lijuan Huang, Yeming Xiong‘ research review from School of Economics and Management, Xidian University, published in Advances in Internet of Things Journal 2013 Vol. 3 by Scientific Research Publishing
Table 2. U-R model calculation example.
Table 3. U-R model calculations.
In microblog networks, the description of friend relationship varies with service providers. For instance, when we use SINA microblog, the relationship is “follow and followed”, while using Tencent microblog, it is “listen and listened”. In this paper, we adopt the “follow and followed”, shown inFigure 1. For example, if user A follows user B, A is a Follower to B, whereas B is a Followee to A.
Currently, microblog is the most popular online social network, for it has not only the characteristics of the social network, but also clear ones of media, it is also called “social media”. View All Papers, please click here
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