Abstract
With the expansion of social networks, there is a rise in rumours and misinformation being shared among the users. Rumours have the potential to harm people in various ways. The spread of rumours in the network can be prevented by feeding users the true information. In this paper, we model a social network with multiple users connected to each other, where we aim to protect a specified set of target nodes in the network which are deemed as more vulnerable to the spread of a particular rumour as compared to the other nodes in the network. A linear threshold model with one direction state transition (LT1DT) for propagation of information in the social network, which is a modified version of the LT model, is considered in the paper. To counteract the spread of rumours, we select a group of nodes in the network as anti-rumour seed nodes using different selection algorithms to spread the truth in the network. We determine the average number of rumour infected target nodes in the network for random selection, random target node selection, max degree selection and greedy selection algorithm. The average number of rumour infected target nodes for different anti-rumour seed selection algorithms are compared in the paper. We compare the average number of rumour infected target nodes to the varying target set size for different anti-rumour seed selection algorithms. The results demonstrate the effectiveness and efficiency of the different algorithms on real world data set, and how target set size influence our results.
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Bora, S., Rao, S. (2022). Target Node Protection from Rumours in Online Social Networks. In: Verma, P., Charan, C., Fernando, X., Ganesan, S. (eds) Advances in Data Computing, Communication and Security. Lecture Notes on Data Engineering and Communications Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-16-8403-6_53
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DOI: https://doi.org/10.1007/978-981-16-8403-6_53
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