Who Sets the Tone? Determining the Impact of Convergence Behaviour Archetypes in Social Media Crisis Communication

  • Milad MirbabaieEmail author
  • Deborah Bunker
  • Stefan Stieglitz
  • Annika Deubel


Convergence Behaviour Archetypes (CBA) describe the behavioural traits of individuals who spontaneously and collectively move towards emergency situations. If convergence is not managed effectively, unintended crisis management issues may emerge and lead to an exacerbation of the crisis situation. Social media users express different behavioural intentions while converging on a crisis. While these behavioural intentions have been analysed in previous research, an understanding of Convergence Behaviour facilitated by social media use to an effective and smart level of control, is yet to be achieved. Manual content and social network analyses were conducted on our Twitter dataset of the Manchester Bombing 2017 and this analysis identified three dominant convergence behaviour archetypes i.e. the Helpers, the Mourners and the Detectives. These archetypes had the highest crisis communications impact regarding their retweet behaviour. This work provides a better theoretical understanding of Convergence Behaviour archetype influence and impact on crisis communication, for Information Systems research and practice.


Convergence behaviour Crisis communication Social media Social network analysis Information systems 



  1. Acar, A., & Muraki, Y. (2011). Twitter for crisis communication: lessons learned from Japan’s tsunami disaster. International Journal of Web Based Communities, 7(3), 392.Google Scholar
  2. Akhgar, B., Fortune, D., Hayes, R. E., Manso, M., & Guerra, B. (2013). Social Media in Crisis Events. In Proceedings of the IEEE International Conference on Technologies for Homeland Security (pp. 760–765).Google Scholar
  3. Albris, K. (2017). The switchboard mechanism: How social media connected citizens during the 2013 floods in Dresden. Journal of Contingencies and Crisis Management, 1–8.Google Scholar
  4. Auf der Heide, E. (2003). Convergence behavior in disasters. Annals of Emergency Medicine, 41(4), 463–466.Google Scholar
  5. Bunker, D., Mirbabaie, M., & Stieglitz, S. (2017). Convergence Behaviour of Bystanders : An Analysis of 2016 Munich Shooting Twitter Crisis Communication. In Proceedings of the Australasian Conference on Information Systems.Google Scholar
  6. Bunker, D., & Sleigh, A. (2016). Social Media Use and Convergence Behaviours During Disasters: A Cloud with a Silver Lining or a Fog of Manipulation? In Proceedings of the Information Systems Research Conference Scandinavia.Google Scholar
  7. Bunker, D., Sleigh, T., Levine, L., & Ehnis, C. (2015). Disaster Management: Building Resilient Systems to Aid Recovery. In Research proceedings from the Bushfire and Natural Hazards CRC & AFAC conference (pp. 1–6).Google Scholar
  8. Cameron, M. A., Power, R., Robinson, B., & Yin, J. (2012). Emergency situation awareness from twitter for crisis management. In Proceedings of the 21st international conference companion on World Wide Web - WWW ‘12 Companion (p. 695).Google Scholar
  9. Cha, M., Haddai, H., Benevenuto, F., & Gummadi, K. P. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. In International AAAI Conference on Weblogs and Social Media (pp. 10–17).Google Scholar
  10. Dwivedi, Y. K., Kelly, G., Janssen, M., Rana, N. P., Slade, E. L., & Clement, M. (2018). Social Media: The Good, the Bad, and the Ugly. Information Systems Frontiers, 20(3), 419–423. Scholar
  11. Fischer, D., Posegga, O., & Fischbach, K. (2016). Communication Barriers in Crisis Management: A Literature Review. In Proceedings of the 2016 European Conference of Information Systems.Google Scholar
  12. Fritz, C. E., & Mathewson, J. H. (1957). Convergence Behavior in Disasters: A Problem in Social Control. American Sociological Review, 23.Google Scholar
  13. Ghosh, S., Ghosh, K., Ganguly, D., Chakraborty, T., Jones, G. J. F., Moens, M. F., & Imran, M. (2018). Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends. Information Systems Frontiers, 20(5), 901–907. Scholar
  14. Giner-Sorolla, R., & Maitner, A. T. (2013). Angry at the Unjust, Scared of the Powerful: Emotional Responses to Terrorist Threat. Personality and Social Psychology Bulletin, 39(8), 1069–1082.Google Scholar
  15. Girtelschmid, S., Salfinger, A., Pröll, B., Retschitzegger, W., & Schwinger, W. (2016). Near Real-time Detection of Crisis Situations. In The 39st International ICT Convention MIPRO 2016 (pp. 247–252).Google Scholar
  16. Golbeck, J. (2013). Analyzing the Social Web (1st ed.). Burlington: Morgen Kaufmann.Google Scholar
  17. Gupta, A., Joshi, A., & Kumaraguru, P. (2012). Identifying and Characterizing User Communities on Twitter during Crisis Events. In Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media - DUBMMSM ‘12.Google Scholar
  18. Hagen, L., Keller, T., Neely, S., DePaula, N., & Robert-Cooperman, C. (2017). Crisis Communications in the Age of Social Media: A Network Analysis of Zika-Related Tweets. Social Science Computer Review, 35(4), 1–19.Google Scholar
  19. He, X., Lu, D., Margolin, D., Wang, M., Idrissi, S. El, & Lin, Y.-R. (2017). The Signals and Noise: Actionable Information in Improvised Social Media Channels During a Disaster. In Proceedings of the 2017 ACM on Web Science Conference - WebSci ‘17 (pp. 33–42).Google Scholar
  20. Hong, L., Torrens, P., Fu, C., & Frias-Martinez, V. (2017). Understanding citizens’ and local governments’ digital communications during natural disasters: The case of snowstorms. In Proceedings of the ACM Web Science Conference (pp. 141–150).Google Scholar
  21. Houston, J. B., Hawthorne, J., Perreault, M. F., Park, E. H., Hode, M. G., Halliwell, M. R., et al. (2015). Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters, 39(1), 1–22.Google Scholar
  22. Huang, Y. L., Starbird, K., Orand, M., Stanek, S. A., & Pedersen, H. T. (2015). Connected through crisis: emotional proximity and the spread of misinformation online. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ‘15) (pp. 969–980). doi:10.1145/2675133.2675202.Google Scholar
  23. Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing Social Media Messages in Mass Emergency: A Survey. ACM Computing Surveys, 47(4), 67.Google Scholar
  24. Jenkins, H. (2006). Convergence Culture: Where Old and New Media Collide (1st ed.). New York: New York University Press.Google Scholar
  25. Kapidzic, S., Neuberger, C., Stieglitz, S., & Mirbabaie, M. (2018). Interaction and Influence on Twitter. Digital Journalism, 1–22.Google Scholar
  26. Kendra, J. M., & Wachtendorf, T. (2003). Reconsidering Convergence and Converger Legitimacy in Response To the World Trade Center Disaster. Research in Social Problems and Public Policy, 11(03), 97–122.Google Scholar
  27. Knuth, D., Szymczak, H., Kuecuekbalaban, P., & Schmidt, S. (2016). Social Media in Emergencies - How Useful Can They Be. In Information and Communication Technologies for Disaster Management (ICT-DM).Google Scholar
  28. Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion on social networks. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 111(29).
  29. Laudy, C., Ruini, F., Zanasi, A., Przybyszewski, M., & Stachowicz, A. (2017). Using Social Media in Crisis Management. SOTERIA Fusion Center for Managing Information Gaps. In Proceedings of FUSION 2017, 20th International Conference on Information Fusion (pp. 1855–1862).Google Scholar
  30. Leon, R. D., Rodríguez-Rodríguez, R., Gómez-Gasquet, P., & Mula, J. (2016). Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization. Technological Forecasting and Social Change, 114(2017), 103–118.Google Scholar
  31. Lindsay, B. R. (2011). Social Media and Disasters: Current Uses, Future Options and Policy Considerations. Congressional Research Service Reports.Google Scholar
  32. Lingzi, H., Fu, C., Wu, J., & Frias-Martinez, V. (2018). Information Needs and Communication Gaps between Citizens and Local Governments Online during Natural Disasters. Information Systems Frontiers, 20(5), 1027–1039. Scholar
  33. Liu, B. F., Austin, L., & Jin, Y. (2011). How publics respond to crisis communication strategies: The interplay of information form and source. Public Relations Review, 37(4), 345–353.Google Scholar
  34. Liu, F., & Xu, D. (2018). Social Roles and Consequences in Using Social Media in Disasters: a Structurational Perspective. Information Systems Frontiers, 20(4), 693–711. Scholar
  35. Lozano, E., & Vaca, C. (2017). Crisis management on Twitter: Detecting emerging leaders. In Proceedings of the International Conference on eDemocracy and eGovernment (pp. 140–147).Google Scholar
  36. Ludwig, T., Kotthaus, C., Reuter, C., van Dongen, S., & Pipek, V. (2017). Situated crowdsourcing during disasters: Managing the tasks of spontaneous volunteers through public displays. International Journal of Human-Computer Studies, 102, 103–121. Scholar
  37. Maguen, S., & Litz, B. (2008). Coping with the threat of terrorism: A review. Anxiety, Stress & Coping, 21(1), 570–591.Google Scholar
  38. Mendoza, M., Poblete, B., & Castillo, C. (2010). Twitter Under Crisis: Can we trust what we RT? In Proceedings of the First Workshop on Social Media Analytics (pp. 71–79).Google Scholar
  39. Mirbabaie, M., & Zapatka, E. (2017). Sensemaking in Social Media Crisis Communication - A Case Study on the Brussels Bombings in 2016. In Proceedings of the 25th European Conference on Information Systems (Vol. 138, pp. 2169–2186).Google Scholar
  40. Mondal, T., Pramanik, P., Bhattacharya, I., Boral, N., & Ghosh, S. (2018). Analysis and Early Detection of Rumors in a Post Disaster Scenario. Information Systems Frontiers, 20(5), 961–979. Scholar
  41. Mønsted, B., Sapieżyński, P., Ferrara, E., & Lehmann, S. (2017). Evidence of complex contagion of information in social media: An experiment using Twitter bots. PLoS One, 12(9), e0184148. Scholar
  42. Moore, J., Magee, S., Gamreklidze, E., & Kowalewski, J. (2017). Social Media Mourning: Using Grounded Theory to Explore How People Grieve on Social Networking Sites. Journal of Death and Dying.
  43. Morris, C., & Rubin, S. (2013). Backpacking, Social Media, and Crises: A Discussion of Online Social Convergence. In Information and Communication Technologies in Tourism 2013 (pp. 207–217).Google Scholar
  44. Muhongya, K. V., & Maharaj, M. S. (2015). Visualising and analysing online social networks. In Proceedings of the International Conference on Computing, Communication and Security.Google Scholar
  45. Mukkamala, A., & Beck, R. (2017). Presence of Social Presence during Disasters. In Proceedings of the Pacific Asia Conference on Information Systems.Google Scholar
  46. Nazer, T. H., Xue, G., Ji, Y., & Liu, H. (2017). Intelligent Disaster Response via Social Media Analysis - A Survey. ACM SIGKDD Explorations, 19(1), 46–59.Google Scholar
  47. Ogie, R. I., Forehead, H., Clarke, R. J., & Perez, P. (2018). Participation Patterns and Reliability of Human Sensing in Crowd-Sourced Disaster Management. Information Systems Frontiers, 20(4), 713–728. Scholar
  48. Palen, L. (2008). Online Social Media in Crisis Events. Educause Quarterly, 31(3), 76–78.Google Scholar
  49. Palen, L., & Liu, S. B. (2007). Citizen communications in crisis: anticipating a future of ICT-supported public participation. In Proceedings of the Human Factors in Computing Systems.Google Scholar
  50. Perikos, I., Hatzilygeroudis, I., Makris, C., & Tsakalidis, A. (2014). Modeling ReTweet Diffusion Using Emotional Content. In Proceedings of the Internatoinal Conference on Artificial Intelligence Applications and Innovations (pp. 101–110).Google Scholar
  51. Perry, R. W., & Lindell, M. K. (2003). Understanding Citizen Response to Disasters with Implications for Terrorism. Journal of Contingencies and Crisis Management, 11(2), 49–60.Google Scholar
  52. Pervin, N., Takeda, H., & Toriumi, F. (2014). Factors Affecting Retweetability: An Event-Centric Analysis on Twitter. In International Conference of Information Systems (pp. 1–10).Google Scholar
  53. Ramluckan, T. (2016). Factors affecting the use of social media as a crisis communication tool in South Africa. In Proceedings of the IST-Africa 2016 Conference (pp. 1–11).Google Scholar
  54. Shaw, F., Burgess, J., Crawford, K., & Bruns, A. (2013). Sharing news, making sense, saying thanks: patterns of talk on Twitter during the Queensland floods. Australian Journal of Communication, 40(1), 23–40.Google Scholar
  55. Starbird, K., & Palen, L. (2011). "Voluntweeters”: Self-Organizing by Digital Volunteers in Times of Crisis. In Proceedings of the Conference on Human Factors in Computing Systems.Google Scholar
  56. Stieglitz, S., & Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social Network Analysis and Mining, 3(4), 1277–1291.Google Scholar
  57. Stieglitz, S., Meske, C., Ross, B., & Mirbabaie, M. (2018a). Going Back in Time to Predict the Future - The Complex Role of the Data Collection Period in Social Media Analytics. Information Systems Frontiers. Google Scholar
  58. Stieglitz, S., Mirbabaie, M., & Fromm, J. (2017). Understanding Sense-Making on Social Media During Crises: Categorization of Sense-Making and Strategies. International Journal of Information Systems for Crisis Respponse and Management, 9(4), 49–69.Google Scholar
  59. Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018b). Social media analytics – Challenges in topic discovery, data collection, and data preparation. International Journal of Information Management, 39(2), 156–168.Google Scholar
  60. Subba, R., & Bui, T. (2010). An Exploration of Physical-Virtual Convergence Behaviors in Crisis Situations. In Proceedings of the Annual Hawaii International Conference on System Sciences (pp. 1–10).Google Scholar
  61. Tandoc, E. C., Jr., & Takahashi, B. (2017). Log in if you survived: Collective coping on social media in the aftermath of Typhoon Haiyan in the Philippines. New Media & Society, 19(11), 1778–1793.Google Scholar
  62. Tim, Y., Yang, L., Pan, S. L., Kaewkitipong, L., & Ractham, P. (2013). The Emegernce of Social Media as Boundary Objects in Crisis Response: A Collective Action Perspectiv. In Proceedings of the Thirty Fourth International Conference on Information Systems (ICIS) (Vol. 39, pp. 196–215). doi:
  63. Valecha, R. (2019). An Investigation of Interaction Patterns in Emergency Management: A Case Study of The Crash of Continental Flight 3407. Information Systems Frontiers, 1–13.
  64. Vieweg, S., Palen, L., Liu, S. B., Hughes, A. L., & Sutton, J. (2008). Collective Intelligence in Disaster : Examination of the Phenomenon in the Aftermath of the 2007 Virginia Tech Shooting. In Proceedings of the International Association for Information Systems for Crisis Management (pp. 44–54).Google Scholar
  65. von Sikorski, C., Schmuck, D., Matthes, J., & Binder, A. (2017). “Muslims are not Terrorists”: Islamic State Coverage, Journalistic Differentiation Between Terrorism and Islam, Fear Reactions, and Attitudes Toward Muslims. Mass Communication and Society, 20(6), 825–848.Google Scholar
  66. Xu, W. W., Sang, Y., Blasiola, S., & Park, H. W. (2014). Predicting Opinion Leaders in Twitter Activism Networks. American Behavioral Scientist, 58(10), 1278–1293.Google Scholar
  67. Zhao, D., & Rosson, M. B. (2009). How and Why People Twitter: The Role that Micro-blogging Plays in Informal Communication at Work. In Proceedings of the ACM International Conference on Supporting Group Work (pp. 243–252).Google Scholar

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Authors and Affiliations

  1. 1.University of Duisburg-EssenDuisburgGermany
  2. 2.The University of SydneySydneyAustralia

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