Analysis of Cluster Relationship and Probability between Individuals and Clusters for the Social Network Services at Facebook.com

• Main Authors: Shih-Ling Wang, 王詩齡
• Other Authors: Bo-chiuan Su
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/13661084764582122155

碩士 === 國立東華大學 === 數位知識管理碩士學位學程 === 98 === Online Social Network Service (SNS) is very popular in recent years. This can allow users to expand their social worlds quickly, such as the Facebook.com‘s membership has reached 460 million. It causes interesting phenomenon about virtual real interpersonal relationship. Will it create cluster effect if users build friendships through Social network service? What are personal backgrounds will affect these cluster style? Are there any main information senders within the clusters who can send information quickly and correctly to the appropriate role? In addition, how to analyze and predict the probability of connection between the individuals and the clusters? These research studies have not yet been studied on Literature of Social Network Service. The study derives the related hypothesis from establishing research patterns. The search information is focused on the Social Network Service site─Facebook.com. The research is studied through the channels on the campus and outside the campus. it analyses the difference about the results of two samples. Outside the campus is seeking for volunteers to participate the equipment from the facebook.com and the survey of the Bulletin Board System (BBS)─National Taiwan University Postal Telegraph and Telephone (PPT). On the campus is not only asking for people to participate the equipment from National Dong Hwa University’s teachers and students through community exposition of fans and clubs related to National Dong Hwa University at Facebook.com and also having convenience sampling. The total number of people including on and outside the campus participating in this research experiment is 226. The total number of friendship is 34,681 and it is formed a total 627,804 relation links. The study founds that all the four kinds of relation type, such as family relationships, student relationships, colleague relationships and friend relationships will affect on the information of the clusters significantly. Besides, personal background has great affect on relation types of clusters. The number of friends is the factor for influencing cluster effect significantly, however it doesn’t have significant impact on whether having main information sender in the clusters or not, but main information sender can shorten information flow. In addition, according to the collective data, the study establishes and predicts the probability of connection between the individuals and the clusters and derives relationship pattern function. It also proves that it is not easy to create the clusters on Online Social Network Service by making friends randomly. The study not only develops relationship pattern function of Social Network Service, but also provides true conditional probability of cluster. Besides, the study can predict the probability of connection between the individuals and the clusters and provide market segmentation of Internet.