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We propose an information propagation model that captures important temporalaspects that have been well observed in the dynamics of fake news diffusion, incontrast with the diffusion of truth. The model accounts for differentialpropagation rates of truth and misinformation and for user reaction times. Westudy a time-sensitive variant of the \textit{misinformation mitigation}problem, where $k$ seeds are to be selected to activate a truth campaign so asto minimize the number of users that adopt misinformation propagating through asocial network. We show that the resulting objective is non-submodular andemploy a sandwiching technique by defining submodular upper and lower boundingfunctions, providing data-dependent guarantees. In order to enable the use of areverse sampling framework, we introduce a weighted version of reversereachability sets that captures the associated differential propagation ratesand establish a key equivalence between weighted set coverage probabilities andmitigation with respect to the sandwiching functions. Further, we propose anoffline reverse sampling framework that provides $(1 - 1/e -\epsilon)$-approximate solutions to our bounding functions and introduce animportance sampling technique to reduce the sample complexity of our solution.Finally, we show how our framework can provide an anytime solution to theproblem. Experiments over five datasets show that our approach outperformsprevious approaches and is robust to uncertainty in the model parameters.

Misinformation Mitigation under Differential Propagation Rates and Temporal Penalties