Data · dataset · 2026
Algorithmic Power, Visibility-Based Credibility, and Audience Engagement with Cause-Related Content: An Audience-Side Test of Platform-Mediated Communication
Listed in Teesside University Research Data Repository
Platform algorithms increasingly determine which messages reach audiences on social media, yet most communication research on cause-related content has examined source-side characteristics rather than how audiences interpret the platform itself.
Description
This study tests whether perceived algorithmic power shapes engagement with cause-related content directly, and whether that relationship is partially mediated by a visibility-based credibility heuristic — the inference that messages a platform makes prominent are messages worth attending to.
Survey data were collected from 580 social media users across five English-speaking markets. Confirmatory factor analysis supported a clean three-factor measurement model with strong reliabilities (α = .77–.83) and clear discriminant validity (HTMT < .42); Harman's single-factor test indicated common-method variance was unlikely a major concern. Regression analyses with controls supported all four hypotheses: perceived algorithmic power predicted both engagement (β = .34, p < .001) and visibility-based credibility (β = .30, p < .001), and visibility-based credibility independently predicted engagement (β = .20, p < .001).
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A bias-corrected and accelerated bootstrap mediation analysis (5,000 resamples) produced a significant indirect effect (ab = .061, 95% BCa CI [.035, .092]), indicating partial mediation accounting for approximately 18% of the total effect. Findings suggest that audiences treat platform-curated visibility as a relevance cue, with implications for how applied communicators design cause-related campaigns under algorithmic conditions.
Links
Where it is published
- DOI doi.org/10.17632/wvmkzy7ys4.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Earth & Environmental Science
Provenance · 1 source records, 6 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/wvmkzy7ys4.1 | 6 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |