Opinion Dynamics of Online Social Network Users: A Micro-Level Analysis
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Abstract (arXiv)
In this paper, we present an empirical study of the opinion dynamics of a large-scale sample of online social network users. We estimate opinions of users as continuous scalars based on their subscriptions to information sources and analyze how friendship connections affect the dynamics of these estimations. Distinguishing between positive (toward opinions of friends) and negative (away from opinions of friends) opinion shifts, we find that the existence and magnitude of opinion shifts are positively related (largely through a linear form or an inverted U-shaped form) to the degree of divergence in opinions between user and their friends. Additionally, we moderate the balance between positive and negative shifts using opinion divergence: if the opinions of the focal user and their friends are too similar or dissimilar, there is a relatively low chance of a positive shift.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.
4 papers.
- 2023RDGSL: Dynamic Graph Representation Learning with Structure Learningcited by 13
- 2022Inference of Media Bias and Content Quality Using Natural-Language Processingcited by 0
- 2023Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithmscited by 0
- 2025Wisdom of Crowds Through Myopic Self-Confidence Adaptationcited by 0
- 2025Wisdom of Crowds Through Myopic Self-Confidence Adaptation22 Jun 2025
- 2023RDGSL: Dynamic Graph Representation Learning with Structure Learning5 Sep 2023
- 2023Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithms25 Jun 2023
- 2022Inference of Media Bias and Content Quality Using Natural-Language Processing1 Dec 2022