Abstract
The S-shaped signatures from connective action events offers insights into past social science theories that support characterizing these events through a quantitative approach. The observational approach taken in this analysis builds on the diffusion of innovations theory, critical mass theory, and previous s-shaped production function research to provide ideas for modeling future campaigns. A key benefit to this approach is that these technologies have been studied extensively and the adoption curves from online social movements are platform independent and occur across a range of industries, technologies, and platforms. For this analysis we analyze 9 misinformation and public discourse hashtags which cover a range of topics related to COVID-19 such as lockdowns, face masks, and vaccines. Plotting the cumulative frequency of Tweets from January 1 to December 31, 2020 we observe s-curve signatures representing the adoption of connective action for these campaigns. We then categorize each of these campaigns by examining their affordance and interdependence relationships by assigning retweets, mentions, and original tweets to the type of relationship they exhibit. This will help researchers understand the relationships between users, characterize what actions take place, and how information flows through users as adoption occurs. The contribution of this analysis provides a foundation for mathematical characterization of connective action signatures, and further, offers ideas on how to design support or countermeasures for the type of campaign taking place. The first approach will drive future work toward developing a predictive model, while the affordance approach will help us to understand the organizational components.
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Notes
- 1.
Due to space limitations and to improve image quality, we are presenting a representative sample of the results.
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Acknowledgments
This research is funded in part by the U.S. National Science Foundation (OIA-1946391, OIA-1920920, IIS-1636933, ACI-1429160, and IIS-1110868), U.S. Office of Naval Research (N00014-10-1-0091, N00014-14-1-0489, N00014-15-P-1187, N00014-16-1-2016, N00014-16-1-2412, N00014-17-1–2675, N00014-17-1-2605, N68335-19-C-0359, N00014-19-1-2336, N68335-20-C-0540), U.S. Air Force Research Lab, U.S. Army Research Office (W911NF-20-1-0262, W911NF-16-1-0189), U.S. Defense Advanced Research Projects Agency (W31P4Q-17-C-0059), Arkansas Research Alliance, the Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock, and the Australian Department of Defense Strategic Policy Grants Program (SPGP) (award number: 2020-106-094). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. The researchers gratefully acknowledge the support.
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Spann, B., Maleki, M., Mead, E., Buchholz, E., Agarwal, N., Williams, T. (2021). Using Diffusion of Innovations Theory to Study Connective Action Campaigns. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2021. Lecture Notes in Computer Science(), vol 12720. Springer, Cham. https://doi.org/10.1007/978-3-030-80387-2_13
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