Abstract
Due to the extensive role of social networks in social media, it is easy for people to share the news, and it spreads faster than ever before. These platforms also have been exploited to share the rumor or fake information, which is a threat to society. One method to reduce the impact of fake information is making people aware of the correct information based on hard proof. In this work, first, we propose a propagation model called Competitive Independent Cascade Model with users’ Bias (CICMB) that considers the presence of strong user bias towards different opinions, believes, or political parties. We further propose a method, called \(k-TruthScore\), to identify an optimal set of truth campaigners from a given set of prospective truth campaigners to minimize the influence of rumor spreaders on the network. We compare \(k-TruthScore\) with state of the art methods, and we measure their performances as the percentage of the saved nodes (nodes that would have believed in the fake news in the absence of the truth campaigners). We present these results on a few real-world networks, and the results show that \(k-TruthScore\) method outperforms baseline methods.
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Saxena, A., Saxena, H., Gera, R. (2020). k-TruthScore: Fake News Mitigation in the Presence of Strong User Bias. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_10
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