Abstract
This article explores how Twitter’s algorithmic timeline influences exposure to different types of external media. We use an agent-based testing method to compare chronological timelines and algorithmic timelines for a group of Twitter agents that emulated real-world archetypal users. We first find that algorithmic timelines exposed agents to external links at roughly half the rate of chronological timelines. Despite the reduced exposure, the proportional makeup of external links remained fairly stable in terms of source categories (major news brands, local news, new media, etc.). Notably, however, algorithmic timelines slightly increased the proportion of “junk news” websites in the external link exposures. While our descriptive evidence does not fully exonerate Twitter’s algorithm, it does characterize the algorithm as playing a fairly minor, supporting role in shifting media exposure for end users, especially considering upstream factors that create the algorithm’s input—factors such as human behavior, platform incentives, and content moderation. We conclude by contextualizing the algorithm within a complex system consisting of many factors that deserve future research attention.
Original language | English (US) |
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Journal | Social Media and Society |
Volume | 7 |
Issue number | 3 |
DOIs | |
State | Published - 2021 |
Funding
The authors thank the Social Science Research Council and participants in the \u201CNews Quality in the Platform Era\u201D workshop for their feedback on an early draft of this paper. We especially thank Johanna Dunaway, Efrat Nechushtai, and Emily Vraga for their constructive feedback. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by NSF grant, Award IIS-1717330 The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by NSF grant, Award IIS-1717330
Keywords
- algorithm auditing
- algorithms
- journalism
- news
- platforms
- social media
ASJC Scopus subject areas
- Cultural Studies
- Communication
- Computer Science Applications