Abstract
Misinformation spreading over online social networks is becoming more and more critical due to the huge amount of information sources whose reliability is hard to establish; moreover, several humans psychology factors as echo chambers and biased searches, plus the intensive use of bot, makes the scenario difficult to cope with. Unprecedented opportunities of gathering data to enhance knowledge though raised, even if the threat of assuming a fake as real or viceversa has been hugely increased, so urgent questions are how to ascertain the truth, and how to somehow limit the flooding process of fakes. In this work, we investigate on the diffusion of true, false and mixed news through the Twitter network using a free large dataset of fact-checked rumor cascades, that were also categorized into specific topics (here, we focus on Terrorism and War). Our goal is to assess how news spread depending on their veracity and we also try to provide an analytic formulation of spreading process via a differential equation that approximates this phenomenon by properly setting the retweet rate.
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Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G., Previti, M. (2018). Terrorism and War: Twitter Cascade Analysis. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_27
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DOI: https://doi.org/10.1007/978-3-319-99626-4_27
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