Curating Quality? How Twitter's Timeline Algorithm Treats Different Types of News

Bibliographic Data

ID2414341
AuthorsJack Bandy (0000-0003-3584-2999, Northwestern University, corresponding author), Nicholas Diakopoulos (0000-0001-5005-6123, Northwestern University)
Year2021
Volume7
Issue3
Publication date2021-07-01
Open AccessYes
TypeARTICLE
VenueSocial Media + Society (JOURNAL)
Journal identifiersISSN: 2056-3051 • E-ISSN: 2056-3051
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/20563051211041648
OpenAlexW3198804996
LanguageEN
Citations received15
References cited46

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

Algorithm · Incentive · Machine learning · Moderation · Statistics · Timeline · World Wide Web · Computer Science · Mathematics · Misinformation and Its Impacts · Opinion Dynamics and Social Influence · Social Media and Politics

Tools

Open DOIOpen Access