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. 2021 Jan 14;11(1):1355.
doi: 10.1038/s41598-020-80353-7.

Stochastic events can explain sustained clustering and polarisation of opinions in social networks

Scott A Condie  1   2 , Corrine M Condie  3   4
Affiliations

Stochastic events can explain sustained clustering and polarisation of opinions in social networks

Scott A Condie et al. Sci Rep. .

Abstract

Understanding the processes underlying development and persistence of polarised opinions has been one of the key challenges in social networks for more than two decades. While plausible mechanisms have been suggested, they assume quite specialised interactions between individuals or groups that may only be relevant in particular contexts. We propose that a more broadly relevant explanation might be associated with the influence of external events. An agent-based bounded-confidence model has been used to demonstrate persistent polarisation of opinions within populations exposed to stochastic events (of positive and negative influence) even when all interactions between individuals are noisy and assimilative. Events can have a large impact on the distribution of opinions because their influence acts synchronistically across a large proportion of the population, whereas an individual can only interact with small numbers of other individuals at any particular time.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
(a) Conditions under which individuals are influenced; and (b) workflow within each model timestep. Note that homophily is a system characteristic and hence confidence threshold is the same for all individuals, whereas certainty varies between individuals.
Figure 2
Figure 2
Opinions of individuals starting from a random distribution [− 1 1] under a range of conditions (first 250 timesteps shown).
Figure 3
Figure 3
Conflict (ΔOt) tracked over 1000 timesteps, starting from a uniform random distribution of opinions (ΔO0=1/3=0.577). Shown are dependencies on: (a) confidence threshold when there were no events; (b) average number of connections per individual per timestep when there were no events; (c) confidence interval when there were events; (d) event strength; (e) event frequency; and (f) event duration.
Figure 4
Figure 4
Average population conflict calculated from model results (over the period t = 500–1000 timesteps) plotted against the functional relationship defined by Eq. (6). The correlation was significant (r = 0.99, N = 17, p < 0.001) with a slope of 0.75.
See this image and copyright information in PMC

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