This paper outlines a set of techniques for modelling information contagion in social media, drawing on comprehensive data on the network structure of and communicative activities in the Australian Twittersphere as the basis for the development of contagion simulation approaches.
In doing so, we take into account two distinct aspects of information contagion on Twitter:
The paper draws on a comprehensive dataset describing the follower network structure between the 2.8 million accounts in the entire Australian Twittersphere, first established in 2013 (Bruns et al., 2014) and updated again in 2016; and on the continuous tracking of public tweeting activity by these 2.8 million accounts through the TrISMA infrastructure (Bruns et al., 2016). We use these data to simulate the effects of a range of possible communication strategies on a network structure that accurately replicates the real-world Twitter follower network in Australia, with a focus especially on the area of crisis communication.
This enables a range of modelling experiments that address two central questions: 1) the impact that targetting accounts with certain characteristics during the early phases of the crisis communication process has on the overall dissemination of emergency messages; and 2) the impact that using Twitter-specific communicative features – e.g. a topical hashtag – has on the dissemination of emergency messages. We compare results from these simulations with datasets collected from Twitter around a number of critical events, including the Brisbane floods and the Sydney siege. Although both these events may be described as “crises”, they are qualitatively different: the first event impacted on a large geographical area and on a large number of people, either as an actual or a potential threat; the second was located at a single point, and directly impacted only on a small number of people, but was the focus of attention for many who were located at a significant distance from the actual event location.
The outcomes from this work provide both important new methodological impulses for the modelling of realistic information contagion processes in social media, and directly actionable insights into the specific processes of information contagion in crisis contexts within the Australian Twittersphere.
Bruns, A., & Burgess, J. (2015). Twitter Hashtags from Ad Hoc to Calculated Publics. In N. Rambukkana (Ed.), Hashtag Publics: The Power and Politics of Discursive Networks (pp. 13–28). New York: Peter Lang.
Bruns, A., Burgess, J., & Highfield, T. (2014). A “Big Data” Approach to Mapping the Australian Twittersphere. In P. L. Arthur & K. Bode (Eds.), Advancing Digital Humanities: Research, Methods, Theories (pp. 113–129). Houndmills: Palgrave Macmillan.
Bruns, A., J. Burgess, J. Banks, D. Tjondronegoro, A. Dreiling, J. Hartley, T. Leaver, A. Aly, T. Highfield, R. Wilken, E. Rennie, D. Lusher, M. Allen, D. Marshall, K. Demetrious, & T. Sadkowsky. (2015). TrISMA: Tracking Infrastructure for Social Media Analysis. http://www.trisma.org/.