Limiting the Spread of Misinformation While Effectively Raising Awareness in Social Networks

In this paper, we study the Misinformation Containment (MC) problem. In particular, taking into account the faster development of misinformation detection techniques, we mainly focus on the limiting the misinformation with known sources case. We prove that under the Competitive Activation Model, the MC problem is NP-hard and show that it cannot be approximated in polynomial time within a ratio of \({e}/(e-1)\) unless \(NP \subseteq DTIME (n^{O(\log \log n)})\). Due to its hardness, we propose an effective algorithm, exploiting the critical nodes and using the greedy approach as well as applying the CELF heuristic to achieve the goal. Comprehensive experiments on real social networks are conducted, and results show that our algorithm can effectively expand the awareness of correct information as well as limit the spread of misinformation.