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Message diffusion through social network service: The case of rumor and non-rumor related tweets during Boston bombing 2013

Author

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  • Jaeung Lee

    (State University of New York at Buffalo)

  • Manish Agrawal

    (University of South Florida)

  • H. R. Rao

    (State University of New York at Buffalo)

Abstract

Social Network Services (SNS) such as Twitter play a significant role in reporting media, particularly during the extreme events. We examined the impact of tweet features on the diffusion of two types of messages during 2013 Boston marathon tragedy—rumor related and non-rumor related (both in the context of the Boston tragedy). Negative binomial analysis revealed that tweet features such as reaction time, number of followers, and usage of hashtag have an impact on tweet message diffusion during the tragedy. The number of followers showed a positive relationship with message diffusion. However, the relationship between tweet reaction time and message diffusion was negative. Finally, tweet messages that did not include hashtags diffused more than messages that contained hashtags. This paper contributes by adapting the innovation diffusion model to explore tweet message diffusion in Twitter space during extreme events.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:5:d:10.1007_s10796-015-9568-z
    DOI: 10.1007/s10796-015-9568-z
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    1. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
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    2. 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).
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    4. Marta Poblet & Esteban García-Cuesta & Pompeu Casanovas, 0. "Crowdsourcing roles, methods and tools for data-intensive disaster management," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    5. Peng, Peng & Poon, Jessie P.H. & Yang, Yu & Lu, Feng & Cheng, Shifen, 2019. "Global oil traffic network and diffusion of influence among ports using real time data," Energy, Elsevier, vol. 172(C), pages 333-342.
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    7. Mingxin Gan & Lily Sun & Rui Jiang, 2019. "GLORY: Exploration and integration of global and local correlations to improve personalized online social recommendations," Information Systems Frontiers, Springer, vol. 21(4), pages 925-939, August.
    8. Babak Abedin & Abdul Babar, 2018. "Institutional vs. Non-institutional use of Social Media during Emergency Response: A Case of Twitter in 2014 Australian Bush Fire," Information Systems Frontiers, Springer, vol. 20(4), pages 729-740, August.
    9. Chul Woo Yoo, 2020. "An Exploration of the Role of Service Recovery in Negative Electronic Word-of-Mouth Management," Information Systems Frontiers, Springer, vol. 22(3), pages 719-734, June.
    10. Mohammad Alamgir Hossain & Yogesh K. Dwivedi & Caroline Chan & Craig Standing & Abdus-Samad Olanrewaju, 2018. "Sharing Political Content in Online Social Media: A Planned and Unplanned Behaviour Approach," Information Systems Frontiers, Springer, vol. 20(3), pages 485-501, June.
    11. Yu‐Ru Lin & Drew Margolin & Xidao Wen, 2017. "Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1580-1605, August.
    12. Chittaranjan Hota & Shambhu Upadhyaya & Jamal Nazzal Al-Karaki, 2015. "Advances in secure knowledge management in the big data era," Information Systems Frontiers, Springer, vol. 17(5), pages 983-986, October.
    13. Miyoung Chong & Hae Jung Maria Kim, 2020. "Social roles and structural signatures of top influentials in the #prayforparis Twitter network," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 315-333, February.
    14. Marta Poblet & Esteban García-Cuesta & Pompeu Casanovas, 2018. "Crowdsourcing roles, methods and tools for data-intensive disaster management," Information Systems Frontiers, Springer, vol. 20(6), pages 1363-1379, December.
    15. Nan Jing & Zhao Wu & Shanshan Lyu & Vijayan Sugumaran, 2021. "Information credibility evaluation in online professional social network using tree augmented naïve Bayes classifier," Electronic Commerce Research, Springer, vol. 21(2), pages 645-669, June.
    16. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    17. Han Zheng & Dion Hoe‐Lian Goh & Edmund Wei Jian Lee & Chei Sian Lee & Yin‐Leng Theng, 2022. "Understanding the effects of message cues on COVID‐19 information sharing on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(6), pages 847-862, June.
    18. Kathrin Eismann, 2021. "Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter," Journal of Business Economics, Springer, vol. 91(9), pages 1299-1329, November.

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