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Novel fake news spreading model with similarity on PSO-based networks

Author

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  • Fan, Dongmei
  • Jiang, Guo-Ping
  • Song, Yu-Rong
  • Li, Yin-Wei

Abstract

This paper proposes a fake news spreading model with similarity taken into account, assuming that the similarity between individuals can affect the transmission rate. Simulations show that the similarity of two connected nodes and the product of their degrees are positively correlated when the network temperature is small, and the similarity of two connected nodes decreases as the product of their degrees increases. Thus the transmission rate can be expressed as the function of their degrees in the proposed model. The theoretic analysis demonstrates the critical threshold is related to both the influence coefficient and the similarity function. Simulation results show a smaller influence coefficient leads to a larger critical threshold and smaller final density of stiflers.

Suggested Citation

  • Fan, Dongmei & Jiang, Guo-Ping & Song, Yu-Rong & Li, Yin-Wei, 2020. "Novel fake news spreading model with similarity on PSO-based networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    • Handle: RePEc:eee:phsmap:v:549:y:2020:i:c:s0378437120301011
    DOI: 10.1016/j.physa.2020.124319
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    References listed on IDEAS

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    2. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    3. Nekovee, M. & Moreno, Y. & Bianconi, G. & Marsili, M., 2007. "Theory of rumour spreading in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 457-470.
    4. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    5. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
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    Cited by:

    1. Raffaele D’Ambrosio & Giuseppe Giordano & Serena Mottola & Beatrice Paternoster, 2021. "Stiffness Analysis to Predict the Spread Out of Fake Information," Future Internet, MDPI, vol. 13(9), pages 1-10, August.

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