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

TL;DR

This paper introduces a new classification system for user-facing cues in social media interfaces to improve understanding and transparency of algorithms, aiding comparison and accountability across platforms.

Contribution

It develops a comprehensive taxonomy of UI elements that reveal algorithmic decisions, covering design form, information content, and user agency, applicable across multiple social media platforms.

Findings

Created a standard classification system for algorithm transparency cues.

Applied the taxonomy to six major social media platforms.

Provides a reference for designing more transparent and accountable social media interfaces.

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.