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Research Note —Role of Social Media in Social Change: An Analysis of Collective Sense Making During the 2011 Egypt Revolution

Author

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  • Onook Oh

    (Information Systems Program, Business School, University of Colorado Denver, Denver, Colorado 80202)

  • Chanyoung Eom

    (Department of Finance, Hanyang University Business School, Seoul, South Korea 133-791)

  • H. R. Rao

    (Management Science and Systems Department, State University of New York at Buffalo, Buffalo, New York 14260)

Abstract

This study explores the role of social media in social change by analyzing Twitter data collected during the 2011 Egypt Revolution. Particular attention is paid to the notion of collective sense making, which is considered a critical aspect for the emergence of collective action for social change. We suggest that collective sense making through social media can be conceptualized as human-machine collaborative information processing that involves an interplay of signs, Twitter grammar, humans, and social technologies. We focus on the occurrences of hashtags among a high volume of tweets to study the collective sense-making phenomena of milling and keynoting. A quantitative Markov switching analysis is performed to understand how the hashtag frequencies vary over time, suggesting structural changes that depict the two phenomena. We further explore different hashtags through a qualitative content analysis and find that, although many hashtags were used as symbolic anchors to funnel online users’ attention to the Egypt Revolution, other hashtags were used as part of tweet sentences to share changing situational information. We suggest that hashtags functioned as a means to collect information and maintain situational awareness during the unstable political situation of the Egypt Revolution.

Suggested Citation

  • Onook Oh & Chanyoung Eom & H. R. Rao, 2015. "Research Note —Role of Social Media in Social Change: An Analysis of Collective Sense Making During the 2011 Egypt Revolution," Information Systems Research, INFORMS, vol. 26(1), pages 210-223, March.
  • Handle: RePEc:inm:orisre:v:26:y:2015:i:1:p:210-223
    DOI: 10.1287/isre.2015.0565
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    References listed on IDEAS

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    1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    2. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    3. Karl E. Weick & Kathleen M. Sutcliffe & David Obstfeld, 2005. "Organizing and the Process of Sensemaking," Organization Science, INFORMS, vol. 16(4), pages 409-421, August.
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    9. Cole Vaughn & Kathleen Sherman-Morris & Philip Poe, 2023. "Factors influencing retweeting of local news media tweets during Hurricane Irma," 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. 119(1), pages 583-611, October.
    10. Arpan Kumar Kar, 2021. "What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”," Information Systems Frontiers, Springer, vol. 23(5), pages 1341-1361, September.
    11. Paul M. Leonardi, 2018. "Social Media and the Development of Shared Cognition: The Roles of Network Expansion, Content Integration, and Triggered Recalling," Organization Science, INFORMS, vol. 29(4), pages 547-568, August.
    12. Son, Jaebong & Lee, Hyung Koo & Jin, Sung & Lee, Jintae, 2019. "Content features of tweets for effective communication during disasters: A media synchronicity theory perspective," International Journal of Information Management, Elsevier, vol. 45(C), pages 56-68.
    13. 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.
    14. Christine Abdalla Mikhaeil & Richard Baskerville, 2019. "Using semiotics to analyze representational complexity in social media," Post-Print hal-02509212, HAL.
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