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The understanding of how users in a network update their opinions based ontheir neighbours opinions has attracted a great deal of interest in the fieldof network science, and a growing body of literature recognises thesignificance of this issue. In this research paper, we propose a new dynamicmodel of opinion formation in directed networks. In this model, the opinion ofeach node is updated as the weighted average of its neighbours opinions, wherethe weights represent social influence. We define a new centrality measure as asocial influence metric based on both influence and conformity. We measure thisnew approach using two opinion formation models: (i) the Degroot model and (ii)our own proposed model. Previously published research studies have notconsidered conformity, and have only considered the influence of the nodes whencomputing the social influence. In our definition, nodes with low in-degree andhigh out-degree that were connected to nodes with high out-degree and lowin-degree had higher centrality. As the main contribution of this research, wepropose an algorithm for finding a small subset of nodes in a social networkthat can have a significant impact on the opinions of other nodes. Experimentson real-world data demonstrate that the proposed algorithm significantlyoutperforms previously published state-of-the-art methods.

Opinion Leader Detection in Online Social Networks Based on Output and Input Links