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

Condie, Scott A.; Condie, Corrine M.

doi:10.1038/s41598-020-80353-7

Cited by 12 publications

(34 citation statements)

References 54 publications

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“…Exogenous Shocks. Following Condie and Condie (22), we model exogenous shocks as potentially unifying events, such as a global pandemic, economic collapse, attack by a foreign adversary or terrorist (whether foreign or domestic), or impending climatic catastrophe. The shock is implemented by creating an additional issue on which all agents strongly agree.…”

Section: Significancementioning

confidence: 99%

Polarization and tipping points

Macy

Tabin

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Research has documented increasing partisan division and extremist positions that are more pronounced among political elites than among voters. Attention has now begun to focus on how polarization might be attenuated. We use a general model of opinion change to see if the self-reinforcing dynamics of influence and homophily may be characterized by tipping points that make reversibility problematic. The model applies to a legislative body or other small, densely connected organization, but does not assume country-specific institutional arrangements that would obscure the identification of fundamental regularities in the phase transitions. Agents in the model have initially random locations in a multidimensional issue space consisting of membership in one of two equal-sized parties and positions on 10 issues. Agents then update their issue positions by moving closer to nearby neighbors and farther from those with whom they disagree, depending on the agents’ tolerance of disagreement and strength of party identification compared to their ideological commitment to the issues. We conducted computational experiments in which we manipulated agents’ tolerance for disagreement and strength of party identification. Importantly, we also introduced exogenous shocks corresponding to events that create a shared interest against a common threat (e.g., a global pandemic). Phase diagrams of political polarization reveal difficult-to-predict transitions that can be irreversible due to asymmetric hysteresis trajectories. We conclude that future empirical research needs to pay much closer attention to the identification of tipping points and the effectiveness of possible countermeasures.

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Section: Significancementioning

confidence: 99%

Polarization and tipping points

Macy

Tabin

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…The transition matrix encodes the influence processes in the model and may reflect a broad set of effects including the persuasiveness of individuals as a function of their current positions and general trends in opinion change caused by influential external events 31 . For example, political debates or interviews could increase the reputation of a particular person and, thus, strengthen the persuasiveness of arguments in support of this person.…”

Section: Methodsmentioning

confidence: 99%

“…An important challenge in the field of opinion dynamics models is to determine the settings under which the model would be able to generate stable opinion polarization. Possible solutions here, apart from negative influence, are mass media 26 , social feedback processes 25 , social influence structure 27 , arguments exchange 28 , 29 , social identity 30 , external events 31 , or mistrust 32 . Recently, the issue of opinion polarization was successfully explained by using concepts from machine learning theory 33 .…”

Section: Literaturementioning

confidence: 99%

A general framework to link theory and empirics in opinion formation models

Kozitsin

2022

Sci Rep

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We introduce a minimal opinion formation model that is quite flexible and can reproduce a wide variety of the existing micro-influence assumptions and models. The model can be easily calibrated on real data, upon which it imposes only a few requirements. From this perspective, our model can be considered as a bridge, connecting theoretical studies on opinion formation models and empirical research on social dynamics. We investigate the model analytically by using mean-field approximation and numerically via Monte Carlo simulations. Our analysis is exemplified by recently reported empirical data drawn from an online social network. We demonstrate that the model calibrated on these data may reproduce fragmented and polarizing social systems. Furthermore, we manage to generate an artificial society that features properties quantitatively and qualitatively similar to those observed empirically at the macro scale. This ability became possible after we had advanced the model with two important communication features: selectivity and personalization algorithms.

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“…The transition matrix encodes the influence processes in the model and may reflect a broad set of effects including persuasiveness of individuals as a function of their current positions and general trends in opinion change caused by influential external events 29 . Further, the transition matrix should be sensitive to such events and transform as a response to them.…”

Section: Methodsmentioning

confidence: 99%

“…An important challenge in the field of opinion dynamics models is to determine the settings under which the model would be able to generate stable opinion polarization. Possible solutions here, apart from negative influence, are mass media 24 , social feedback processes 23 , social influence structure 25 , arguments exchange 26,27 , social identity 28 , external events 29 , or mistrust 30 .…”

Section: Literaturementioning

confidence: 99%

Linking Theory and Empirics: A General Framework to Model Opinion Formation Processes

Kozitsin

2021

Preprint

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We introduce a minimal opinion formation model, which is quite flexible and can reproduce a broad variety of the existing micro-influence assumptions and models. At the same time, the model can be easily calibrated on real data, upon which it imposes only a few requirements. From this perspective, our model can be considered as a bridge, connecting theoretical studies on opinion formation models and empirical research on social dynamics. We investigate the model analytically by using mean-field approximation and numerically. Our analysis is exemplified by recently reported empirical data drawn from an online social network. We demonstrate that the model calibrated on these data may reproduce fragmented and polarizing social systems. Furthermore, we manage to generate an artificial society that features properties quantitatively and qualitatively similar to those observed empirically at the macro scale. This ability became possible after we had advanced the model with two important communication features: selectivity and personalization algorithms.

show abstract

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

Cited by 12 publications

References 54 publications

Polarization and tipping points

Polarization and tipping points

A general framework to link theory and empirics in opinion formation models

Linking Theory and Empirics: A General Framework to Model Opinion Formation Processes

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