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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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    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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    Cited by:

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    4. 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.
    5. Irina Heimbach & Oliver Hinz, 2018. "The Impact of Sharing Mechanism Design on Content Sharing in Online Social Networks," Information Systems Research, INFORMS, vol. 29(3), pages 592-611, September.
    6. 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.
    7. Amit Kumar Kushwaha & Ruchika Pharswan & Prashant Kumar & Arpan Kumar Kar, 2023. "How Do Users Feel When They Use Artificial Intelligence for Decision Making? A Framework for Assessing Users’ Perception," Information Systems Frontiers, Springer, vol. 25(3), pages 1241-1260, June.
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    9. Christine Abdalla Mikhaeil & Richard Baskerville, 2019. "Using semiotics to analyze representational complexity in social media," Post-Print hal-02509212, HAL.
    10. Jaebong Son & Jintae Lee & Kai R. Larsen & Jiyoung Woo, 2020. "Understanding the uncertainty of disaster tweets and its effect on retweeting: The perspectives of uncertainty reduction theory and information entropy," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1145-1161, October.
    11. Chen, Guanghua, 2019. "ILSCR rumor spreading model to discuss the control of rumor spreading in emergency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 88-97.
    12. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    13. Arpan Kumar Kar, 0. "What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”," Information Systems Frontiers, Springer, vol. 0, pages 1-21.
    14. 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.
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    16. 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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