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

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.

[1]  Vineet M. Payyappalli,et al.  Deterrence and Risk Preferences in Sequential Attacker–Defender Games with Continuous Efforts , 2017, Risk analysis : an official publication of the Society for Risk Analysis.

[2]  Dragomir R. Radev,et al.  Rumor has it: Identifying Misinformation in Microblogs , 2011, EMNLP.

[3]  Andrew Whinston,et al.  Sentiment Manipulation in Online Platforms: An Analysis of Movie Tweets , 2017 .

[4]  Mona T. Diab,et al.  Rumor Detection and Classification for Twitter Data , 2015, ArXiv.

[5]  Jiahua Wu,et al.  Liking and Following and the Newsvendor: Operations and Marketing Policies Under Social Influence , 2014, Manag. Sci..

[6]  Rae Zimmerman,et al.  Optimal Resource Allocation for Defense of Targets Based on Differing Measures of Attractiveness , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[7]  Kyounghee Hazel Kwon,et al.  An Exploration of Social Media in Extreme Events: Rumor Theory and Twitter during the Haiti Earthquake 2010 , 2010, ICIS.

[8]  Marco Scarsini,et al.  Monopoly Pricing in the Presence of Social Learning , 2015, Manag. Sci..

[9]  David K. Y. Yau,et al.  Defense of Cyber Infrastructures Against Cyber‐Physical Attacks Using Game‐Theoretic Models , 2016, Risk analysis : an official publication of the Society for Risk Analysis.

[10]  Max Nanis,et al.  Socialbots: voices from the fronts , 2012, INTR.

[11]  Qiaozhu Mei,et al.  Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts , 2015, WWW.

[12]  Hongyu Chen,et al.  De‐Biasing the Reporting Bias in Social Media Analytics , 2016 .

[13]  Yun Liu,et al.  An epidemic model of rumor diffusion in online social networks , 2013 .

[14]  Xiaojun Shan,et al.  Modeling Credible Retaliation Threats in Deterring the Smuggling of Nuclear Weapons Using Partial Inspection - A Three-Stage Game , 2014, Decis. Anal..

[15]  Klaus Krippendorff,et al.  Content Analysis: An Introduction to Its Methodology , 1980 .

[16]  Bairong Wang,et al.  Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: a case study on shelters and ID checks , 2020, Natural Hazards.

[17]  Filippo Menczer,et al.  The rise of social bots , 2014, Commun. ACM.

[18]  J. Zhuang,et al.  Crisis information distribution on Twitter: a content analysis of tweets during Hurricane Sandy , 2017, Natural Hazards.

[19]  Xuanming Su,et al.  Omnichannel Retail Operations with Buy-Online-and-Pickup-in-Store , 2016, Manag. Sci..

[20]  Jun Zhuang,et al.  Regulation Games Between Government and Competing Companies: Oil Spills and Other Disasters , 2012, Decis. Anal..

[21]  Kyomin Jung,et al.  Prominent Features of Rumor Propagation in Online Social Media , 2013, 2013 IEEE 13th International Conference on Data Mining.

[22]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[23]  Ella Segev,et al.  Social Learning and the Design of New Experience Goods , 2017, Manag. Sci..

[24]  Wanlei Zhou,et al.  To Shut Them Up or to Clarify: Restraining the Spread of Rumors in Online Social Networks , 2014, IEEE Transactions on Parallel and Distributed Systems.

[25]  Arkaitz Zubiaga,et al.  Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads , 2015, PloS one.

[26]  Jun Zhuang,et al.  Fire Code Inspection and Compliance: A Game-Theoretic Model Between Fire Inspection Agencies and Building Owners , 2020, Decis. Anal..

[27]  R. H. Knapp,et al.  A PSYCHOLOGY OF RUMOR , 1944 .

[28]  S. van der Linden,et al.  The fake news game: actively inoculating against the risk of misinformation , 2019 .

[29]  T. Shibutani.  Improvised News: A Sociological Study of Rumor , 1966 .

[30]  G. Manimaran,et al.  Cybersecurity for Critical Infrastructures: Attack and Defense Modeling , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[31]  H. Raghav Rao,et al.  Information control and terrorism: Tracking the Mumbai terrorist attack through twitter , 2011, Inf. Syst. Frontiers.

[32]  Li Zeng,et al.  #Unconfirmed: Classifying Rumor Stance in Crisis-Related Social Media Messages , 2016, ICWSM.

[33]  Victoria L. Rubin.  Deception Detection and Rumor Debunking for Social Media , 2017 .

[34]  P. Bordia,et al.  Rumor Psychology: Social and Organizational Approaches , 2006 .

[35]  Adversarial Risk Analysis , 2009 .

[36]  Bairong Wang,et al.  Rumor response, debunking response, and decision makings of misinformed Twitter users during disasters , 2018, Natural Hazards.

[37]  Yao Song,et al.  An exploration of rumor combating behavior on social media in the context of social crises , 2016, Comput. Hum. Behav..

[38]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[39]  Qing Li,et al.  Effect of Digitalized Rumor Clarification on Stock Markets , 2018, Emerging Markets Finance and Trade.

[40]  Yiangos Papanastasiou,et al.  Dynamic Pricing in the Presence of Social Learning and Strategic Consumers , 2017, Manag. Sci..

[41]  Nicholas DiFonzo,et al.  The Watercooler Effect: A Psychologist Explores the Extraordinary Power of Rumors , 2008 .

[42]  Alfonso J. Pedraza-Martinez,et al.  Social Media for Disaster Management: Operational Value of the Social Conversation , 2019, Production and Operations Management.

[43]  Yiangos Papanastasiou.  Fake News Propagation and Detection: A Sequential Model , 2020, Manag. Sci..

[44]  Lei Shi,et al.  She gets a sports car from our donation: rumor transmission in a Chinese microblogging community , 2013, CSCW.

[45]  Ashish Gupta,et al.  Detecting fake news for reducing misinformation risks using analytics approaches , 2019, Eur. J. Oper. Res..

[46]  J. Prasad,et al.  THE PSYCHOLOGY OF RUMOUR: A STUDY RELATING TO THE GREAT INDIAN EARTHQUAKE OF 1934 , 1935 .

[47]  Vicki M. Bier,et al.  Balancing Terrorism and Natural Disasters - Defensive Strategy with Endogenous Attacker Effort , 2007, Oper. Res..

[48]  Arkaitz Zubiaga,et al.  Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations , 2016, COLING.

[49]  Xuanming Su,et al.  A Dynamic Level-k Model in Sequential Games , 2012, Manag. Sci..

[50]  Leon Festinger,et al.  A Study of a Rumor: Its Origin and Spread , 1948 .

[51]  Huaye Li,et al.  Combating Rumor Spread on Social Media: The Effectiveness of Refutation and Warning , 2015, 2015 48th Hawaii International Conference on System Sciences.

[52]  Vicki M. Bier,et al.  Analytical method to identify the number of containers to inspect at U.S. ports to deter terrorist attacks , 2011, Ann. Oper. Res..

[53]  H. Raghav Rao,et al.  Community Intelligence and Social Media Services: A Rumor Theoretic Analysis of Tweets During Social Crises , 2013, MIS Q..