A nature - inspired approach based on Forest Fire model for modeling rumor propagation in social networks

摘要

Rumor propagation is a serious menace faced by online social network users. Modeling rumor propagation helps analyze the diffusion pattern of rumors in social networks and thereby limit the spread of rumors to a considerable extent. There are several factors which affect the extensive spread of rumors in social networks, but the identification of prime features is crucial in modeling the spread of rumors in a faster and more efficient way. In this paper, we propose a novel nature - inspired approach that focuses on the identification of best features to model the diffusion of rumors via online social networks. The proposed approach maps the spread of rumors in social networks to the spread of wildfire in a forest, and the prominent features for modeling rumor dissemination are determined by analyzing the main factors which contribute to the rapid spread of forest fire. We have developed a novel nature - inspired algorithm based on the Forest Fire model that utilizes the identified prominent features to calculate the probability of a node to share a rumor, and it measures the extent of rumor spread across a network by detecting the rumor - affected nodes in the network. The proposed algorithm also serves to examine the circulation path of rumors, the nodes which shared the rumor, and identify the nodes which played a major role in the rumor dissemination process. We evaluated the performance of the proposed method using two datasets obtained from Twitter, and the experimental results illustrate the efficiency of our proposed method and selected features. We also present a rumor propagation graph which aids in the analysis of the rumor diffusion pattern and discovers the key spreaders who are involved in the rumor dispersal. Furthermore, we also provide a feature - level comparison with the various existing approaches for rumor modeling to show how effectively the proposed approach maps the forest fire spread with the problem of rumor diffusion.