Exploring social media users’ experiences with algorithmic transparency cues

All mainstream social media platforms now use algorithms to display recommended content, and some (e.g. Instagram, LinkedIn) have started showing what we call algorithmic transparency cues about why certain posts are recommended. However, little is known about what cues users see on their own feeds and how they experience them. Thus, using an online survey ( N = 515) of adult U.S. social media users, we gathered data about two research questions: (1) What types of algorithmic cues users find in their own feeds, and (2) their experiences with algorithms and their transparency. Content analysis of user-submitted screenshots and cue descriptions shows that most transparency cues refer to users’ behaviors, behaviors of others in their network, and sponsored posts. Furthermore, open-ended responses indicate that users have critical opinions about algorithms, calling for greater algorithmic transparency on social media, and offering suggestions for researchers and platform designers moving forward.

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Publication Details

Journal
New Media & Society
Published
2025-05-19
DOI
https://doi.org/10.1177/14614448251339493
Citations
2
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
4.73

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article

Exploring social media users’ experiences with algorithmic transparency cues

Jiayi Chen, Anne Oeldorf-Hirsch, Lili R. Romann, Isabella Witkowich
2 citations
New Media & Society
Ethics and Social Impacts of AI
4.73
article

Exploring social media users’ experiences with algorithmic transparency cues

Jiayi Chen, Anne Oeldorf-Hirsch, Lili R. Romann, Isabella Witkowich
article en
2 citations

Abstract

All mainstream social media platforms now use algorithms to display recommended content, and some (e.g. Instagram, LinkedIn) have started showing what we call algorithmic transparency cues about why certain posts are recommended. However, little is known about what cues users see on their own feeds and how they experience them. Thus, using an online survey ( N = 515) of adult U.S. social media users, we gathered data about two research questions: (1) What types of algorithmic cues users find in their own feeds, and (2) their experiences with algorithms and their transparency. Content analysis of user-submitted screenshots and cue descriptions shows that most transparency cues refer to users’ behaviors, behaviors of others in their network, and sponsored posts. Furthermore, open-ended responses indicate that users have critical opinions about algorithms, calling for greater algorithmic transparency on social media, and offering suggestions for researchers and platform designers moving forward.

New Media & Society
University of Connecticut (US), Hong Kong Polytechnic University (HK)
University of Connecticut
Openalex Percentile: Top 6%
Ethics and Social Impacts of AI
4.73
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