Temporal Influence Blocking: Minimizing the Effect of Misinformation in Social Networks

• Published in: 2017 IEEE 33rd International Conference on Data Engineering (ICDE) pp. 847 - 858
• Main Authors: Chonggang Song, Wynne Hsu, Mong Li Lee
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-04-2017
• Subjects: Computer science; Conferences; Data engineering; Delay effects; Delays; Drives; Social network services
• ISSN: 2375-026X
• DOI: 10.1109/ICDE.2017.134

The diffusion of rumors is a major concern for web users. Limiting the spread of rumor on social networks has become an important task. One approach is to identify nodes to start a truth campaign such that when users are aware of the truth, they would not believe or propagate the rumor. However, existing works do not take into account the delays of information diffusion or the time point beyond which propagation of misinformation is no longer critical. In this paper, we consider a more realistic situation where information is propagated with delays and the goal is to reduce the number of rumor-infected users before a deadline. We call this the Temporal Influence Blocking (TIB) problem. We propose a two-phase solution called TIB-Solver to select k nodes to start a truth campaign such that the number of users reached by a rumor is minimized. Experiments show that the proposed TIBSolver outperforms the state-of-the-art algorithms in terms of both effectiveness and efficiency.