We propose an information propagation model that captures important temporal aspects that have been well observed in the dynamics of fake news diffusion, in contrast with the diffusion of truth. The model accounts for differential propagation rates of truth and misinformation and for user reaction times. We study a time-sensitive variant of the \textit{misinformation mitigation} problem, where $k$ seeds are to be selected to activate a truth campaign so as to minimize the number of users that adopt misinformation propagating through a social network. We show that the resulting objective is non-submodular and employ a sandwiching technique by defining submodular upper and lower bounding functions, providing data-dependent guarantees. In order to enable the use of a reverse sampling framework, we introduce a weighted version of reverse reachability sets that captures the associated differential propagation rates and establish a key equivalence between weighted set coverage probabilities and mitigation with respect to the sandwiching functions. Further, we propose an offline reverse sampling framework that provides $(1 - 1/e - \epsilon)$-approximate solutions to our bounding functions and introduce an importance sampling technique to reduce the sample complexity of our solution. Finally, we show how our framework can provide an anytime solution to the problem. Experiments over five datasets show that our approach outperforms previous approaches and is robust to uncertainty in the model parameters.
翻译:我们提议了一个信息传播模型,该模型反映了在假冒新闻传播动态中观察到的重要时间方面,与真理的传播相反。模型说明了真理和错误信息的不同传播率和用户反应时间的不同。我们研究了一个时间敏感化的变方 \ textit{misfIfe减缓} 问题,选择了美元种子以启动真相运动,从而最大限度地减少通过社交网络传播错误信息的用户数量。我们表明,由此产生的目标是非子模量性的,并采用三明治技术,界定子模量上下约束功能,提供数据依赖的保证。为了能够使用反向抽样框架,我们引入了一个逆向可达性加权的组合组合,以捕捉到相关的差异传播率,并在加权设定的覆盖概率概率和减少与三明治功能有关的减缓之间确立一个关键等值。此外,我们提议了一个离线反向抽样框架,为我们的捆绑功能提供$-1-e-e-e-emislon近价解决方案,并引入重要的抽样技术,以降低我们解决方案的样本复杂性。最后,我们提出了一个加权的模型,我们展示了我们之前的不确定性的方法。