Abstract
Evaluating the propagation of ideas on social media platforms such as Twitter allows researchers to gain an understanding of the characteristics of online communication patterns. The expansion and popularity of Twitter has increased the instances of the propagation of rumors and misinformation all over the world. To distinguish the dynamics of the spread of misinformation and legitimate hashtags related to COVID-19, in this paper, we utilized the SEIZ (Susceptible, Exposed, Infected, Skeptic) epidemiological model. We evaluated the trend of the propagation of misinformation and legitimate hashtags from three different campaigns: lockdown, face mask, and vaccine. Our findings showed that the propagation of misinformation and legitimate hashtags can be modeled by the SEIZ model. Leveraging a mathematical model can lead to an increased understanding of the trends of the propagation of misinformation and legitimate information on Twitter and ultimately help to provide methods to prevent the propagation of misinformation while promoting the spread of legitimate information.
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Acknowledgements
This research is funded in part by the U.S. National Science Foundation (OIA-1946391, OIA-1920920, IIS-1636933, ACI-1429160, and IIS-1110868), U.S. Office of Naval Research (N00014-10-1-0091, N00014-14-1-0489, N00014-15-P-1187, N00014-16-1-2016, N00014-16-1-2412, N00014-17-1-2675, N00014-17-1-2605, N68335-19-C-0359, N00014-19-1-2336, N68335-20-C-0540, N00014-21-1-2121), U.S. Air Force Research Lab, U.S. Army Research Office (W911NF-20-1-0262, W911NF-16-1-0189), U.S. Defense Advanced Research Projects Agency (W31P4Q-17-C-0059), Arkansas Research Alliance, the Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock, and the Australian Department of Defense Strategic Policy Grants Program (SPGP) (award number: 2020-106-094). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. The researchers gratefully acknowledge the support.
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Maleki, M., Arani, M., Buchholz, E., Mead, E., Agarwal, N. (2021). Applying an Epidemiological Model to Evaluate the Propagation of Misinformation and Legitimate COVID-19-Related Information on Twitter. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2021. Lecture Notes in Computer Science(), vol 12720. Springer, Cham. https://doi.org/10.1007/978-3-030-80387-2_3
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