Text-based Influence Propagation Analysis System for Multiple Role Social Network

• Main Authors: Yi-Chu Chien, 簡亦楚
• Other Authors: Meng-Feng Tsai
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/37543948503926432708

碩士 === 國立中央大學 === 資訊工程學系 === 101 === With the rise of social network service, there are many social behaviors. The most popular behaviors are information sharing and following. Users share their ideas and interesting things with their friends on social network sites. Moreover, users select some people to follow and obtain information. The past researchers focused on the evaluation of influence, based on using the social structure to analyze and find the most influential user. Few researches discussed how the propagation took place between users. And I believe that social network is a heterogeneous network, we cannot just classify a user as a specific topic. User are supposed to have multiple roles in the different topics. With the different influential relationship, user’s identity may be active or passive. So, I propose an integrated analysis system, which supports text-based content and find the valuable feature of social network. The research is composed of four parts. First, I define the hierarchical interesting topic structure generated by data related with targeted users. Second, I try to determine the feature of users according to their activities. User may belong to one interesting topic or many interesting topic at the same time. Third, I extract the influential propagation paths for analysis. The last part is that I build TIPAS (Text-based Influence Propagation Analysis System), which provides analyst to analyze influence which caused propagation paths. For easy understanding, I also use the concept of data visualization to display the analysis results.