Influencing Myself: Self-Reinforcement Through Online Political Expression
Open accessAbstract
Over the past decade, various online communication platforms have empowered citizens to express themselves politically. Although the political impact of online citizen expression has drawn considerable attention, research has largely focused on whether and how citizen-generated messages influence the public as an information alternative to traditional news outlets. The present study aims to provide a new perspective on understanding citizen expression by examining its political implications for the expressers themselves rather than those exposed to the expressed ideas. Data from a national survey and an online discussion forum study suggest that expressing oneself about politics provides self-reinforcing feedback. Political expressions on social media and the online forum were found to (a) reinforce the expressers’ partisan thought process and (b) harden their pre-existing political preferences. Implications for the role the Internet plays in democracy will be discussed.
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Ideological reinforcement was examined by the correlation between affective candidate evaluations and political ideology, which quantifies the extent to which the two are aligned as a function of experimental treatment.
“First, ideological reinforcement was examined by the correlation between affective candidate evaluations and political ideology which quantifies the extent to which the two are aligned as a function of the experimental treatment (Cho et al., 2018).”
From Do Search Algorithms Endanger Democracy? An Experimental Investigation of Algorithm Effects on Political Polarization (2020) · cites this paper as Cho et al., 2018
The Results describe testing whether political expression strengthens the relationship between party identification and political opinions, and the Measures section describes affective ratings of candidates (page numbers unavailable). But the paper tests that relationship with regression models and tracks changes in candidate preference; it does not describe correlating candidate evaluations with ideology as a function of experimental treatment. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMost articles found detrimental associations between digital media and different forms of political polarization.
“Most articles found detrimental associations between digital media and different forms of political polarization114–118.”
From A systematic review of worldwide causal and correlational evidence on digital media and democracy (2022) · cites this paper as [118]
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- Authors
- Cho, J., Ahmed, S., Keum, H., Choi, Y. J., & Lee, J. H.
- Affiliations
- University of California, Davis (US)
- Sungkyunkwan University (KR)
- Ewha Womans University (KR)
- Kyung Hee University (KR)
- Corresponding author
- Cho, Jaeho
- Venue
- Communication Research
- Publisher
- SAGE Publications
- Published
- 2016-04-20
- Volume
- 45
- Issue
- 1
- Pages
- 83-111
- ISSN
- 0093-6502 (ISSN-L)
- 1552-3810
- OpenAlex
- W2336724834
- Cited by
- 116
- References
- 74
- Citations by year
- 2026: 10 · 2025: 18 · 2024: 15 · 2023: 16 · 2022: 15 · 2021: 17 · 2020: 8 · 2019: 8 · 2018: 5 · 2017: 4
- FWCI
- 16.02
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- Communication Research (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- self-reinforcement, online political expression, citizen-generated content, social media, online forums, political polarization, partisan identity, political preferences, feedback effects, internet and democracy
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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