TY - JOUR
T1 - Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News
AU - Bandy, Jack
AU - Diakopoulos, Nicholas
N1 - Funding Information:
The authors thank the Social Science Research Council and participants in the “News Quality in the Platform Era” 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
Funding Information:
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
Publisher Copyright:
© The Author(s) 2021.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Twitter
KW - algorithm auditing
KW - algorithms
KW - journalism
KW - news
KW - platforms
KW - social media
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U2 - 10.1177/20563051211041648
DO - 10.1177/20563051211041648
M3 - Article
AN - SCOPUS:85114317455
SN - 2056-3051
VL - 7
JO - Social Media and Society
JF - Social Media and Society
IS - 3
ER -