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Modelling opinion dynamics in the age of algorithmic personalisation
Nicola Perra1, Luis E C Rocha2
1Centre for Business Network Analysis, Business School, University of Greenwich, SE10 9LS, London, United Kingdom. n.perra@gre.ac.uk.
Scientific Reports
|May 16, 2019
Summary
Algorithmic filtering on social networks can distort opinion dynamics, potentially creating echo chambers and polarization. However, these filtering mechanisms can also be used to regulate opinions and influence viewpoints.
Area of Science:
- Computational social science
- Network science
- Information science
Background:
- Modern technology and online social platforms enable large-scale communication but are limited by user attention.
- Algorithmic personalization is used to manage information overload, potentially distorting exposure to diverse opinions.
- The impact of algorithmic gatekeeping on opinion dynamics in hyper-connected societies remains poorly understood.
Purpose of the Study:
- To devise and analyze an opinion dynamics model on social networks considering algorithmic filtering.
- To investigate how different filtering strategies (random, time-ordered, opinion-based) affect opinion spread.
- To explore the interplay between filtering mechanisms, network topology, and opinion polarization.
Main Methods:
- Development of an opinion dynamics model on a social network.
- Application of filtering algorithms based on random selection, time ordering, and user's current opinion.
- Analysis of filtering effects in conjunction with network features like correlations and heterogeneity.
- Simulation of a central nudging scenario to influence opinions.
Main Results:
- Algorithmic filtering can significantly influence opinion distribution, particularly when biased towards a user's current opinion.
- Networks with topological and spatial correlations amplify filtering effects, leading to echo chambers and polarization.
- Network heterogeneity in connectivity patterns can mitigate the tendency towards polarization.
- Even minimal central nudging can effectively shift the overall opinion landscape.
Conclusions:
- Algorithmic filtering strategies can profoundly shape opinion dynamics on social networks.
- The structure of social networks plays a crucial role in amplifying or mitigating the effects of algorithmic filtering.
- Filtering algorithms and nudging represent potential tools for regulating and influencing public opinion on digital platforms.
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