OPINION DYNAMICS RELATED TO COVID-19 VACCINE HESITANCY AND MEGA-INFLUENCERS ANNA HAENSCH, NATASA DRAGOVIC, CHRISTOPH B ¨ ORGERS, AND BRUCE BOGHOSIAN Abstract. Covid-19 vaccines are widely available in the United States, yet our Covid-19 vaccination rates have remained far below 100%. Data from the CDC show that even in places where vaccine acceptance was proportionally high at the outset of the Covid-19 vac- cination effort, that willingness has not necessarily translated into high rates of vaccination over the subsequent months. We model how such a shift could have arisen, using param- eters in agreement with data from the state of Alabama. The simulations suggest that in Alabama, local interactions would have favored the emergence of tight consensus around the initial majority view, which was to accept the Covid-19 vaccine. Yet this is not what happened. We therefore add to our model the impact of mega-influencers such as mass media, the governor of the state, etc. Our simulations show that a single vaccine-hesitant mega-influencer, reaching a large fraction of the population, can indeed cause the consensus to shift radically, from acceptance to hesitancy. Surprisingly this is true even when the mega-influencer only reaches individuals who are already somewhat inclined to agree with them, and under the conservative assumption that individuals give no more weight to the mega-influencer than they would give to a single one of their friends or neighbors. Our simulations also suggest that a competing mega-influencer with the opposite view can shift the mean population opinion back, but under some conditions cannot restore the tightness of consensus around that view. Our code and data are distributed in the ODyN (Opinion Dynamic Networks) library available at https://github.com/annahaensch/ODyN. 1. Introduction Opinions drive human behavior [1], and opinion formation is a complex multi-scale process, involving characteristics of the individual, local interaction of individuals, social media, mass media etc. Opinion dynamics have been modeled using approaches inspired by physics [2]. A survey of the literature on opinion dynamics can be found in [3]. In this study, we use the example of opinions about Covid-19 vaccination, in part because the topic is of urgent current interest, but also because data are plentiful (see for instance [4], which we use as a source in our simulations), and a significant shift in opinions appears to have occurred in the United States in a short time span. We focus on two scales, local interactions (conversations with friends, family, neighbors, colleagues) and global influencers such as mass media and prominent politicians, whom we refer to as mega-influencers. Social media can belong to either category. A discussion of the merits or perils of Covid-19 vaccination among 25 Facebook friends could be viewed as a local interaction, whereas a Twitter account owner with millions of followers is a mega-influencer. In the model of this paper, social media will not appear explicitly. Despite the apparent geography-less nature of the online world, studies have shown [5] that geographic distance is still a key component in the formation and maintenance of social networks. Therefore, in the present paper we situate individuals in physical and opinion distance, and take this as a model for our network. Date : 8 April 2022. 1 arXiv:2202.00630v2 [physics.soc-ph] 15 Apr 2022