Abstract
In the information age, leaked private information may cause significant physical and mental harm to the relevant parties, leading to a negative social impact. In order to effectively evaluate the impact of such information leakage in today’s social networks, it is necessary to accurately predict the scope and depth of private information diffusion. By doing so, it would be feasible to prevent and control the improper spread and diffusion of private information. In this paper, we propose an intelligent prediction method for private information diffusion in social networks based on comprehensive data analysis. We choose Sina Weibo, one of the most prominent social networks in China, to study. Firstly, a prediction model of message forwarding behavior is established by analyzing the characteristic factors that influence the forwarding behavior of the micro-blog users. Then the influence of users is calculated based on the interaction time and topological structure of users relationship, and the diffusion critical paths are identified. Finally, through the user forwarding probability transmission, we determine the micro-blog diffusion cut-off conditions. The simulation results on Sina Weibo data set show that the prediction accuracy is 86.9%, which indicates that our method is efficient to predict the message diffusion in real-world social networks.
Highlights
One key aspect about today’s social network, such as Sina Weibo, is that they provide a major number of people with a wide range of public opinion space conveniently, users are increasingly accustomed to express their opinions and share information on these network [1]
The simulation results on Sina Weibo data set show that the prediction accuracy is 86.9%, which indicates that our method is efficient to predict the message diffusion in real-world social networks
The information diffusion in social networks is largely influenced by opinion leaders, who can promote the rapid diffusion of information and expand the scope of influence
Summary
One key aspect about today’s social network, such as Sina Weibo, is that they provide a major number of people with a wide range of public opinion space conveniently, users are increasingly accustomed to express their opinions and share information on these network [1]. Based on threshold and forwarding probability, several approaches [4,5] are proposed to predict the forwarding behavior of users in Sina Weibo These methods use large scale recursive loop of nodes in social networks to generate the prediction results, which causes high cost in the calculation of the algorithms. An influence rate concept is proposed to describe the users’ influence according to activeness, dissemination degrees and number of fans [10] It would be beneficial if we could take key uses and critical paths into account to predict the private information diffusion. This paper proposes a method to predict the spread of private information by considering the users’ privacy concern as a factor.
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