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Real-Time and Cost-Effective Limitation of Misinformation Propagation

Online Social Networks (OSNs) constitute one of the most important communication channels and are widely utilized as news sources. Information spreads widely and rapidly in OSNs through the word-of-mouth effect. However, it is not uncommon for misinformation to propagate in the network. Misinformation dissemination may lead to undesirable effects, especially in cases where the non-credible information concerns emergency events. Therefore, it is essential to timely limit the propagation of misinformation. Towards this goal, we suggest a novel propagation model, namely the Dynamic Linear Threshold (DLT) model, that effectively captures the way contradictory information, i.e., misinformation and credible information, propagates in the network. The DLT model considers the probability of a user alternating between competing beliefs, assisting in either the propagation of misinformation or credible news. Based on the DLT model, we formulate an optimization problem that aims in identifying the most appropriate subset of users to limit the spread of misinformation by initiating the propagation of credible information. Through extensive experimental evaluation we demonstrate that our approach outperforms its competitors.