Rumor Propagation Detection System in Social Network Services

Abstract

The growing use of the smart device such as smartphones and tablets has resulted in increasing number of social network service (SNS) users recently. SNS allows a fast propagation and it is used as a tool to send information. But its negative sides need to be considered. In this paper, we analyzed actual data of malicious accounts and extracted features. Based on this results, we detect the suspected accounts that spread rumors. Firstly, we crawled actual data and analyzed feature. And we selected feature as three approaches and added a new feature as propagation approach by existing work. That is user can re-tweet influencer’s tweet and edit it. We discussed it by ratio for RT. After that, we selected classification standard using average of data based on selected feature and trained it. Bayesian network is used for training. And the system may provide a new classification through re-analysis of the data. Proposed system is that the accuracy is 91.94 % and F-measure is 93.76 %.