IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v298y2022i2p714-733.html
   My bibliography  Save this article

Interplay of rumor propagation and clarification on social media during crisis events - A game-theoretic approach

Author

Listed:
  • Agarwal, Puneet
  • Aziz, Ridwan Al
  • Zhuang, Jun

Abstract

For a rapid dissemination of information during crisis events, official agencies and disaster relief organizations have been utilizing social media platforms, which are susceptible to rumor propagation. To minimize the impact of rumors with limited time and resources, the agencies and social media companies not only need to wisely choose the cases to clarify amongst the numerous cases, but they should also make an informed decision on the timing of clarification. Reacting fast can be misjudged as an obvious best policy as partial/imprecise information may fail to contain the impact of the rumors. On the other hand, investment in terms of time, effort, and money to clarify with more complete information also allows the rumors to spread with their full force during the learning phase, thereby making the process of decision-making very challenging. The objective of this paper is to determine the optimal strategies for the official agencies and social media companies by developing two novel sequential game-theoretic models, namely “Rumor Selection for Clarification” and “Learning for Rumor Clarification”, that can help decide which rumor to clarify and when to clarify, respectively. Results from this study indicate that posting verified information on social media reduces the uncertainties involved in rumor transmission, thereby enabling social media users to make informed decisions on whether to support or oppose the rumor being circulated. This verification needs to be obtained within reasonable limits of time and cost to keep the learning process worthwhile.

Suggested Citation

  • Agarwal, Puneet & Aziz, Ridwan Al & Zhuang, Jun, 2022. "Interplay of rumor propagation and clarification on social media during crisis events - A game-theoretic approach," European Journal of Operational Research, Elsevier, vol. 298(2), pages 714-733.
    • Handle: RePEc:eee:ejores:v:298:y:2022:i:2:p:714-733
    DOI: 10.1016/j.ejor.2021.06.060
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221721005919
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2021.06.060?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lu (Lucy) Yan & Alfonso J. Pedraza‐Martinez, 2019. "Social Media for Disaster Management: Operational Value of the Social Conversation," Production and Operations Management, Production and Operations Management Society, vol. 28(10), pages 2514-2532, October.
    2. Jon Roozenbeek & Sander van der Linden, 2019. "The fake news game: actively inoculating against the risk of misinformation," Journal of Risk Research, Taylor & Francis Journals, vol. 22(5), pages 570-580, May.
    3. Yiangos Papanastasiou & Nicos Savva, 2017. "Dynamic Pricing in the Presence of Social Learning and Strategic Consumers," Management Science, INFORMS, vol. 63(4), pages 919-939, April.
    4. Vicki M. Bier & Naraphorn Haphuriwat & Jaime Menoyo & Rae Zimmerman & Alison M. Culpen, 2008. "Optimal Resource Allocation for Defense of Targets Based on Differing Measures of Attractiveness," Risk Analysis, John Wiley & Sons, vol. 28(3), pages 763-770, June.
    5. Bairong Wang & Jun Zhuang, 2018. "Rumor response, debunking response, and decision makings of misinformed Twitter users during disasters," 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. 93(3), pages 1145-1162, September.
    6. 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.
    7. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    8. Jun Zhuang & Vicki M. Bier, 2007. "Balancing Terrorism and Natural Disasters---Defensive Strategy with Endogenous Attacker Effort," Operations Research, INFORMS, vol. 55(5), pages 976-991, October.
    9. Fei Gao & Xuanming Su, 2017. "Omnichannel Retail Operations with Buy-Online-and-Pick-up-in-Store," Management Science, INFORMS, vol. 63(8), pages 2478-2492, August.
    10. Hongyu Chen & Zhiqiang (Eric) Zheng & Yasin Ceran, 2016. "De-Biasing the Reporting Bias in Social Media Analytics," Production and Operations Management, Production and Operations Management Society, vol. 25(5), pages 849-865, May.
    11. Pnina Feldman & Yiangos Papanastasiou & Ella Segev, 2019. "Social Learning and the Design of New Experience Goods," Management Science, INFORMS, vol. 65(5), pages 1502-1519, April.
    12. Onook Oh & Manish Agrawal & H. Raghav Rao, 2011. "Information control and terrorism: Tracking the Mumbai terrorist attack through twitter," Information Systems Frontiers, Springer, vol. 13(1), pages 33-43, March.
    13. Ming Hu & Joseph Milner & Jiahua Wu, 2016. "Liking and Following and the Newsvendor: Operations and Marketing Policies Under Social Influence," Management Science, INFORMS, vol. 62(3), pages 867-879, March.
    14. Puneet Agarwal & Kyle Hunt & Shivasubramanian Srinivasan & Jun Zhuang, 2020. "Fire Code Inspection and Compliance: A Game-Theoretic Model Between Fire Inspection Agencies and Building Owners," Decision Analysis, INFORMS, vol. 17(3), pages 208-226, September.
    15. Vineet M. Payyappalli & Jun Zhuang & Victor Richmond R. Jose, 2017. "Deterrence and Risk Preferences in Sequential Attacker–Defender Games with Continuous Efforts," Risk Analysis, John Wiley & Sons, vol. 37(11), pages 2229-2245, November.
    16. Xiaojun (Gene) Shan & Jun Zhuang, 2014. "Modeling Credible Retaliation Threats in Deterring the Smuggling of Nuclear Weapons Using Partial Inspection---A Three-Stage Game," Decision Analysis, INFORMS, vol. 11(1), pages 43-62, March.
    17. Vicki Bier & Naraphorn Haphuriwat, 2011. "Analytical method to identify the number of containers to inspect at U.S. ports to deter terrorist attacks," Annals of Operations Research, Springer, vol. 187(1), pages 137-158, July.
    18. May Cheung & Jun Zhuang, 2012. "Regulation Games Between Government and Competing Companies: Oil Spills and Other Disasters," Decision Analysis, INFORMS, vol. 9(2), pages 156-164, June.
    19. 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.
    20. 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.
    21. Zhang, Chaowei & Gupta, Ashish & Kauten, Christian & Deokar, Amit V. & Qin, Xiao, 2019. "Detecting fake news for reducing misinformation risks using analytics approaches," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1036-1052.
    22. Yiangos Papanastasiou, 2020. "Fake News Propagation and Detection: A Sequential Model," Management Science, INFORMS, vol. 66(5), pages 1826-1846, May.
    23. Insua, Insua Rios & Rios, Jesus & Banks, David, 2009. "Adversarial Risk Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 841-854.
    24. Teck-Hua Ho & Xuanming Su, 2013. "A Dynamic Level-k Model in Sequential Games," Management Science, INFORMS, vol. 59(2), pages 452-469, March.
    25. Jun Wang & Zhilong Xie & Qing Li & Jinghua Tan & Rong Xing & Yuanzhu Chen & Fengyun Wu, 2019. "Effect of Digitalized Rumor Clarification on Stock Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(2), pages 450-474, January.
    26. Nageswara S. V. Rao & Stephen W. Poole & Chris Y. T. Ma & Fei He & Jun Zhuang & David K. Y. Yau, 2016. "Defense of Cyber Infrastructures Against Cyber‐Physical Attacks Using Game‐Theoretic Models," Risk Analysis, John Wiley & Sons, vol. 36(4), pages 694-710, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Yi & Xu, Jiuping & Nekovee, Maziar & Li, Zongmin, 2022. "The impact of official rumor-refutation information on the dynamics of rumor spread," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    2. Liu, Kaiyi & Yuan, Xianghui & Wang, Chen & Hou, Wenxuan, 2022. "Silence is golden? Responses to rumors by Chinese listed firms," Finance Research Letters, Elsevier, vol. 49(C).
    3. Kumar, Ajay & Taylor, James W., 2024. "Feature importance in the age of explainable AI: Case study of detecting fake news & misinformation via a multi-modal framework," European Journal of Operational Research, Elsevier, vol. 317(2), pages 401-413.
    4. Lian, Ying & Tang, Huiting & Xiang, Mengting & Dong, Xuefan, 2024. "Public attitudes and sentiments toward ChatGPT in China: A text mining analysis based on social media," Technology in Society, Elsevier, vol. 76(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hunt, Kyle & Zhuang, Jun, 2024. "A review of attacker-defender games: Current state and paths forward," European Journal of Operational Research, Elsevier, vol. 313(2), pages 401-417.
    2. Vineet M. Payyappalli & Jun Zhuang & Victor Richmond R. Jose, 2017. "Deterrence and Risk Preferences in Sequential Attacker–Defender Games with Continuous Efforts," Risk Analysis, John Wiley & Sons, vol. 37(11), pages 2229-2245, November.
    3. Puneet Agarwal & Kyle Hunt & Shivasubramanian Srinivasan & Jun Zhuang, 2020. "Fire Code Inspection and Compliance: A Game-Theoretic Model Between Fire Inspection Agencies and Building Owners," Decision Analysis, INFORMS, vol. 17(3), pages 208-226, September.
    4. Mohammad E. Nikoofal & Jun Zhuang, 2012. "Robust Allocation of a Defensive Budget Considering an Attacker's Private Information," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 930-943, May.
    5. Li, Yapeng & Qiao, Shun & Deng, Ye & Wu, Jun, 2019. "Stackelberg game in critical infrastructures from a network science perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 705-714.
    6. Zhiheng Xu & Jun Zhuang, 2019. "A Study on a Sequential One‐Defender‐N‐Attacker Game," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1414-1432, June.
    7. Jie Xu & Jun Zhuang, 2016. "Modeling costly learning and counter-learning in a defender-attacker game with private defender information," Annals of Operations Research, Springer, vol. 236(1), pages 271-289, January.
    8. Hunt, Kyle & Agarwal, Puneet & Zhuang, Jun, 2022. "On the adoption of new technology to enhance counterterrorism measures: An attacker–defender game with risk preferences," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    9. 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).
    10. Peiqiu Guan & Jun Zhuang, 2015. "Modeling Public–Private Partnerships in Disaster Management via Centralized and Decentralized Models," Decision Analysis, INFORMS, vol. 12(4), pages 173-189, December.
    11. Li, Feng & Du, Timon C. & Wei, Ying, 2023. "This is what’s in store for you: How online social learning affects product positioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    12. Lu Xiao & Hang Zhang & Yong Qin, 2020. "Competitive Pricing of Innovative Products with Consumers’ Social Learning," Sustainability, MDPI, vol. 12(9), pages 1-13, May.
    13. Yang, Rui & Tang, Wansheng & Dou, Mengdi & Zhang, Jianxiong, 2021. "Pricing and investing in co-creation with customers for a duopoly," International Journal of Production Economics, Elsevier, vol. 237(C).
    14. Zaiyan Wei & Mo Xiao & Rong Rong, 2021. "Network Size and Content Generation on Social Media Platforms," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1406-1426, May.
    15. Mingyun Gu & Haixiang Guo & Jun Zhuang & Yufei Du & Lijin Qian, 2022. "Social Media User Behavior and Emotions during Crisis Events," IJERPH, MDPI, vol. 19(9), pages 1-21, April.
    16. Wei Wang & Francesco Di Maio & Enrico Zio, 2019. "Adversarial Risk Analysis to Allocate Optimal Defense Resources for Protecting Cyber–Physical Systems from Cyber Attacks," Risk Analysis, John Wiley & Sons, vol. 39(12), pages 2766-2785, December.
    17. Ka Chung Ng & Ping Fan Ke & Mike K. P. So & Kar Yan Tam, 2023. "Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2101-2122, July.
    18. Nan Yang & Renyu Zhang, 2022. "Dynamic pricing and inventory management in the presence of online reviews," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3180-3197, August.
    19. Xiaojun (Gene) Shan & Jun Zhuang, 2014. "Modeling Credible Retaliation Threats in Deterring the Smuggling of Nuclear Weapons Using Partial Inspection---A Three-Stage Game," Decision Analysis, INFORMS, vol. 11(1), pages 43-62, March.
    20. Hausken, Kjell, 2024. "Fifty Years of Operations Research in Defense," European Journal of Operational Research, Elsevier, vol. 318(2), pages 355-368.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:298:y:2022:i:2:p:714-733. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.