Doing Big Things in a Small Way: A Social Media Analytics Approach to Information Diffusion During Crisis Events in Digital Influencer Networks


  • Shohil Kishore The University of Auckland
  • Amy Errmann Auckland University of Technology



information diffusion, public crisis events, digital influencers, influencer network compression, computationally intensive methods


Digital influencers play an essential role in determining information diffusion during crisis events. This paper demonstrates that information diffusion (retweets) on the social media platform Twitter (now X) highly depends on digital influencers’ number of followers and influencers’ location within communication networks. We show (study 1) that there is significantly more information diffusion in regional (vs. national or international) crisis events when tweeted by micro-influencers (vs. meso- and macro-influencers). Further, study 2 demonstrates that this pattern holds when micro-influencers operate in a local location (are located local to the crisis). However, effects become attenuated when micro-influencers are situated in a global location (outside of the locality of the event). We term this effect ‘influencer network compression’ – the smaller in scope a crisis event geography (regional, national, or international) and influencer location (local or global) becomes, the more effective micro-influencers are at diffusing information. This shows that those who possess the most followers (meso- and macro-influencers) are less effective at attracting retweets than micro-influencers situated local to a crisis. As online information diffusion plays a critical role during public crisis events, this paper contributes to both practice and theory by exploring the role of digital influencers and their network geographies in different types of crisis events.


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How to Cite

Kishore, S., & Errmann, A. (2024). Doing Big Things in a Small Way: A Social Media Analytics Approach to Information Diffusion During Crisis Events in Digital Influencer Networks. Australasian Journal of Information Systems, 28.



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