Online social networks provide a convenient platform for the spread of rumors, which could lead to serious aftermaths such as economic losses and public panic. The classical rumor blocking problem aims to launch a set of nodes as a positive cascade to compete with misinformation in order to limit the spread of rumors. However, most of the related researches were based on a one-dimensional diffusion model. In reality, there is more than one feature associated with an object. A user's impression on this object is determined not just by one feature but by her overall evaluation of all features associated with it. Thus, the influence spread of this object can be decomposed into the spread of multiple features. Based on that, we design a multi-feature diffusion model (MF-model) in this paper and formulate a multi-feature rumor blocking (MFRB) problem on a multi-layer network structure according to this model. To solve the MFRB problem, we design a creative sampling method called Multi-Sampling, which can be applied to this multi-layer network structure. Then, we propose a Revised-IMM algorithm and obtain a satisfactory approximate solution to MFRB. Finally, we evaluate our proposed algorithm by conducting experiments on real datasets, which shows the effectiveness of our Revised-IMM and its advantage to their baseline algorithms.

中文翻译：

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多特征扩散模型：社交网络中的谣言拦截


在线社交网络为谣言传播提供了便利的平台，这可能导致经济损失和公众恐慌等严重后果。经典的谣言阻止问题旨在启动一组节点作为正级联与错误信息竞争，以限制谣言的传播。然而，大多数相关研究都是基于一维扩散模型。实际上，一个对象有不止一个特征。用户对该对象的印象不仅取决于某个特征，还取决于她对与其相关的所有特征的总体评价。因此，该对象的影响力传播可以分解为多个特征的传播。在此基础上，本文设计了多特征扩散模型（MF模型），并根据该模型在多层网络结构上制定了多特征谣言阻止（MFRB）问题。为了解决MFRB问题，我们设计了一种创造性的采样方法，称为Multi-Sampling，可以应用于这种多层网络结构。然后，我们提出了Revised-IMM算法并获得了令人满意的MFRB近似解。最后，我们通过在真实数据集上进行实验来评估我们提出的算法，这表明了我们的 Revied-IMM 的有效性及其相对于基线算法的优势。