Time Critical Disinformation Influence Minimization in Online Social Networks

If a piece of disinformation released from a terrorist organization propagates on Twitter and this adversarial campaign is detected after a while, how emergence responders can wisely choose a set of source users to start the counter campaign to minimize the disruptive influence of disinformation in a short time? This practical problem is challenging and critical for authorities to make online social networks a more trustworthy source of information. In this work, we propose to study the time critical disinformation influence minimization problem in online social networks based on a continuous-time multiple campaign diffusion model. We show that the complexity of this optimization problem is NP-hard and provide a provable guaranteed approximation algorithm for this problem by proving several critical properties of the objective function. Experimental results on a sample of real online social network show that the proposed approximation algorithm outperforms various heuristics and the transmission temporal dynamics knowledge is vital for selecting the counter campaign source users, especially when the time window is small.