Limiting the Spread of Misinformation on Multiplex Social Networks

The dissemination of messages countering misinformation is considered a promising approach for limiting the spread of misinformation. On social network, the approach can be posed as a problem, the influence limitation problem. Although most existing studies on the influence limitation problem assume a single-layer structure for social networks, in reality, each individual in society usually has multiple communication channels; moreover, the social network has a multilayer structure. Therefore, this study investigates the problems in limiting the spread of negative influences (i.e., misinformation) in multilayer networks by spreading positive influences (i.e., counter messages against misinformation). Furthermore, we formulate the problem on a two-layered multiplex network by extending the influence limitation problem on a single-layer network. By conducting simulation experiments using synthetic and real multiplex networks, we evaluated the effectiveness of the methods to select seed nodes that trigger the spread of positive influence. The results show that even in two-layered multiplex networks, the seed-node selection methods that use a single-layer structure achieve effectiveness comparable to that of the seed-node selection method that uses both layers of the two-layered network. A method that selects seed nodes from the community boundary nodes can effectively limit the spread of negative influence in most cases.