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Reciprocal spreading and debunking processes of online misinformation: A new rumor spreading–debunking model with a case study

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  • Jiang, Meiling
  • Gao, Qingwu
  • Zhuang, Jun

Abstract

In this digital era, massive digital misinformation was ranked first by the World Economic Forum among the top future global risks. As human and financial resources are limited, governments or companies would like to use the optimal level of debunking effort and the most efficient debunking strategy. There exists a rich literature that studies the rumor spreading process on social media. However, a huge gap exists on studying the simultaneous propagation of false rumors and debunking information, and the interplay between them. The spreading of rumors and anti-rumors is a dynamic and reciprocal process. Acknowledging that effective debunking strategy is a potential tool to reduce the loss of massive digital misinformation, this paper proposes a novel rumor spreading–debunking (RSD) model by ordinary differential equation (ODE) system to explore the interplay mechanism between rumor spreading and debunking processes. We derive and discuss the key factors and parameters that influence the debunking process. Firstly, we consider the spreading pattern of a rumor before Debunkers appear based on the Susceptible–Infected–Recovered (SIR) model with its own characteristics of rumor, and obtain a series of results including the final scope of the rumor spreading, the maximal scale of the rumor spreader, the number of Stiflers at any time point, and the popularity level of the rumor. Secondly, with the data from the real world rumor case, which is the ”Immigration Rumor” during Hurricane Harvey in 2017, we determine the case-specific parameters, and validate our model by comparing the simulated curve with the real data. Our model helps to understand the impact of the rumor on the social media, and predict the future trend. Finally, we use our model to simulate the influence of different debunking strategy, and identify more efficient debunking measures that should be used by the government officials or companies when facing rumor mill under different situations.

Suggested Citation

  • Jiang, Meiling & Gao, Qingwu & Zhuang, Jun, 2021. "Reciprocal spreading and debunking processes of online misinformation: A new rumor spreading–debunking model with a case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    • Handle: RePEc:eee:phsmap:v:565:y:2021:i:c:s0378437120308700
    DOI: 10.1016/j.physa.2020.125572
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    References listed on IDEAS

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    1. Wang, Jiajia & Zhao, Laijun & Huang, Rongbing, 2014. "2SI2R rumor spreading model in homogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 153-161.
    2. Jaeung Lee & Manish Agrawal & H. R. Rao, 2015. "Message diffusion through social network service: The case of rumor and non-rumor related tweets during Boston bombing 2013," Information Systems Frontiers, Springer, vol. 17(5), pages 997-1005, October.
    3. Junjie Zhou & Ying-Ju Chen, 2016. "Targeted Information Release in Social Networks," Operations Research, INFORMS, vol. 64(3), pages 721-735, June.
    4. Mihnea C. Moldoveanu & Joel A. C. Baum, 2011. ""I Think You Think I Think You're Lying": The Interactive Epistemology of Trust in Social Networks," Management Science, INFORMS, vol. 57(2), pages 393-412, February.
    5. Matthew O. Jackson, 2009. "Networks and Economic Behavior," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 489-513, May.
    6. Sinan Aral & Dylan Walker, 2011. "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks," Management Science, INFORMS, vol. 57(9), pages 1623-1639, February.
    7. Bairong Wang & Jun Zhuang, 2017. "Crisis information distribution on Twitter: a content analysis of tweets during Hurricane Sandy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(1), pages 161-181, October.
    8. Shun†Yang Lee & Liangfei Qiu & Andrew Whinston, 2018. "Sentiment Manipulation in Online Platforms: An Analysis of Movie Tweets," Production and Operations Management, Production and Operations Management Society, vol. 27(3), pages 393-416, March.
    9. Kyle Hunt & Bairong Wang & Jun Zhuang, 2020. "Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: a case study on shelters and ID checks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 861-883, August.
    10. Guodong Shi & Alexandre Proutiere & Mikael Johansson & John S. Baras & Karl H. Johansson, 2016. "The Evolution of Beliefs over Signed Social Networks," Operations Research, INFORMS, vol. 64(3), pages 585-604, June.
    11. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    12. Devavrat Shah & Tauhid Zaman, 2016. "Finding Rumor Sources on Random Trees," Operations Research, INFORMS, vol. 64(3), pages 736-755, June.
    13. Zhao, Laijun & Wang, Jiajia & Chen, Yucheng & Wang, Qin & Cheng, Jingjing & Cui, Hongxin, 2012. "SIHR rumor spreading model in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2444-2453.
    14. Zhang, Zi-li & Zhang, Zi-qiong, 2009. "An interplay model for rumour spreading and emergency development," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4159-4166.
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    3. Jamalzadeh, Saeed & Mettenbrink, Lily & Barker, Kash & González, Andrés D. & Radhakrishnan, Sridhar & Johansson, Jonas & Bessarabova, Elena, 2024. "Weaponized disinformation spread and its impact on multi-commodity critical infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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