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The number of followings as an influential factor in rumor spreading

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  • Bodaghi, Amirhosein
  • Goliaei, Sama
  • Salehi, Mostafa

Abstract

In this paper, we hypothesize that the number of followings users have on social networks, has an influence on the impact level of the received rumor/anti-rumor messages by those users. We show that influence as a decreasing exponential coefficient on the probabilities by which users' might get affected from the received messages. Then we put forward another hypothesis that suggests that rumor/anti-rumor posts gradually lose their impact power by passage of time. To assign this time-related feature to the posts, we consider a memory for each agent of the network. Finally, we derive stochastic equations of the new model which incorporate both of the new hypotheses and then evaluate it based on real datasets of rumor spreading on Twitter. The evaluation results support the new hypotheses and show that the novel model is able to better represent rumor spreading on online social networks.

Suggested Citation

  • Bodaghi, Amirhosein & Goliaei, Sama & Salehi, Mostafa, 2019. "The number of followings as an influential factor in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 357(C), pages 167-184.
    • Handle: RePEc:eee:apmaco:v:357:y:2019:i:c:p:167-184
    DOI: 10.1016/j.amc.2019.04.005
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    3. Wang, Jinling & Jiang, Haijun & Hu, Cheng & Yu, Zhiyong & Li, Jiarong, 2021. "Stability and Hopf bifurcation analysis of multi-lingual rumor spreading model with nonlinear inhibition mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    4. T. D. Frank, 2025. "Simplicity of rumor self-organization revealed by unstable eigenvectors and amplitudes," Computational and Mathematical Organization Theory, Springer, vol. 31(1), pages 1-26, March.
    5. Li, Dandan & Qian, Wenqi & Sun, Xiaoxiao & Han, Dun & Sun, Mei, 2023. "Rumor spreading in a dual-relationship network with diverse propagation abilities," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    6. Haji Gul & Feras Al-Obeidat & Adnan Amin & Fernando Moreira & Kaizhu Huang, 2022. "Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    7. Amirhosein Bodaghi & Jonathan J. H. Zhu, 2024. "A big data analysis of the adoption of quoting encouragement policy on Twitter during the 2020 U.S. presidential election," Journal of Computational Social Science, Springer, vol. 7(2), pages 1861-1893, October.

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