Analysis of Trust in Social Community Based on Influence Computation

• Main Authors: Jhao-Ting Yang, 楊詔婷
• Other Authors: Jou-Shien Chang
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/qnj3fj

碩士 === 淡江大學 === 資訊管理學系碩士班 === 104 === Trust and reputation management mechanism is quite simple in the traditional Internet community. The feedback score of a member is usually given by other users or site managers. And, the amount of score is positively correlated with the credibility of the members. Such a simple mechanism is unable to provide effective management for malicious, dishonest or troll members but more likely to be distorted or exploited by those abnormal members, which then affecting the normal development of the Internet community. To solve these problems, this research integrates a variety of social network analysis index of degree, centrality, etc. Using linear regression and Artificial Neural Network to combine these indices by linear and nonlinear manner separately. And, use the result to provide correct influence and trust of social networks members. Secondly, we also consider the influence of the polarity, marked a further stage in the community of good groups and bad groups. In addition, the face of large-scale social network, to calculate various index often waste time and drawn out. This research also explores examine the possibility of using different centrality indices to substitute that of high computation cost. Data download from PTT forum are used for experiment, and the results show the effectiveness of the proposed prediction methods. Secondly, the results also show that the prediction error of influence ranking is less than 25% for more than 70% community members. It demonstrate the effectiveness of the proposed method. We also use four different centrality index to predict the betweenness centrality, and the results were highly correlated, which provides another viable way for reducing the cost of social network analysis.