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Behind the Feed: A Taxonomy of User-Facing Cues for Algorithmic Transparency in Social Media

Haoze Guo, Ziqi Wei

TL;DR

The paper addresses the fragmented landscape of algorithmic transparency cues in social media UIs and proposes an interface-first taxonomy to standardize cues by design form, information content, and user agency. It conducts qualitative content analysis across six platforms, coding 210 cue events to map cues to a three-dimensional taxonomy and examine interaction cost. Key contributions include the taxonomy, a cross-platform cue map, and the identification of three recurring patterns (Displacement, Evidence Scarcity, Uneven Agency) plus a transparency-function gap showing legibility often outpaces verifiability and contestability. This work provides a framework for auditing and guiding design improvements to enhance legibility, verifiability, and contestability of algorithmic decisions in practice, with implications for accountability and policy.

Abstract

People who use social media are learning about how the companies that run these platforms make their decisions on who gets to see what through visual indicators in the interface (UI) of each social media site. These indicators are different for each platform and are not always located in an easy-to-find location on the site. Therefore, it is hard for someone to compare different social media platforms or determine whether transparency leads to greater accountability or only leads to increased understanding. A new classification system has been developed to help provide a standard way of categorizing the way, that an algorithm is presented through UI elements and whether the company has provided any type of explanation as to why they are featured. This new classification system includes the following three areas of development: design form, information content, and user agency. This new classification system can be applied to the six social media platforms currently available and serves as a reference database for identifying common archetypes of features in the each social media platform's UI. The new classification system will assist in determining whether or not the transparency of an algorithm functions the way that it was intended when it was developed and provide future design ideas that can help improve the inspectibility, actionability, and contestability of algorithms.

Behind the Feed: A Taxonomy of User-Facing Cues for Algorithmic Transparency in Social Media

TL;DR

The paper addresses the fragmented landscape of algorithmic transparency cues in social media UIs and proposes an interface-first taxonomy to standardize cues by design form, information content, and user agency. It conducts qualitative content analysis across six platforms, coding 210 cue events to map cues to a three-dimensional taxonomy and examine interaction cost. Key contributions include the taxonomy, a cross-platform cue map, and the identification of three recurring patterns (Displacement, Evidence Scarcity, Uneven Agency) plus a transparency-function gap showing legibility often outpaces verifiability and contestability. This work provides a framework for auditing and guiding design improvements to enhance legibility, verifiability, and contestability of algorithmic decisions in practice, with implications for accountability and policy.

Abstract

People who use social media are learning about how the companies that run these platforms make their decisions on who gets to see what through visual indicators in the interface (UI) of each social media site. These indicators are different for each platform and are not always located in an easy-to-find location on the site. Therefore, it is hard for someone to compare different social media platforms or determine whether transparency leads to greater accountability or only leads to increased understanding. A new classification system has been developed to help provide a standard way of categorizing the way, that an algorithm is presented through UI elements and whether the company has provided any type of explanation as to why they are featured. This new classification system includes the following three areas of development: design form, information content, and user agency. This new classification system can be applied to the six social media platforms currently available and serves as a reference database for identifying common archetypes of features in the each social media platform's UI. The new classification system will assist in determining whether or not the transparency of an algorithm functions the way that it was intended when it was developed and provide future design ideas that can help improve the inspectibility, actionability, and contestability of algorithms.
Paper Structure (16 sections, 3 figures, 5 tables)

This paper contains 16 sections, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Definition and Scope
  4. A Taxonomy of Algorithmic Transparency Cues
  5. Method
  6. Findings
  7. Dataset Overview and Interaction Cost
  8. Three Cross-Platform Patterns: Displacement, Evidence Scarcity, Uneven Agency
  9. Function Gap: Legibility Outruns Accountability
  10. Discussion
  11. Design Implications
  12. Conclusion
  13. Appendix
  14. Compliance by displacement.
  15. Feedback-loop opacity and placebo controls.
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Accountability attributes by decision type. Traceability and contestability are unevenly distributed, concentrating in ads/governance rather than recommendation transparency.
  • Figure 2: Distribution of interaction depth across cue instances (0 indicates co-present cues).
  • Figure 3: Coverage of transparency functions across all cues: legibility (82%), verifiability (14%), and contestability (5%).