Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2021-09-01
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| Series: | Social Media + Society |
| Online Access: | https://doi.org/10.1177/20563051211041648 |
| _version_ | 1818898825170911232 |
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| author | Jack Bandy Nicholas Diakopoulos |
| author_facet | Jack Bandy Nicholas Diakopoulos |
| author_sort | Jack Bandy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T19:38:13Z |
| format | Article |
| id | doaj.art-f9b072de7d5a46069abb9016a6193e66 |
| institution | Directory Open Access Journal |
| issn | 2056-3051 |
| language | English |
| last_indexed | 2024-12-19T19:38:13Z |
| publishDate | 2021-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Social Media + Society |
| spelling | doaj.art-f9b072de7d5a46069abb9016a6193e662022-12-21T20:08:20ZengSAGE PublishingSocial Media + Society2056-30512021-09-01710.1177/20563051211041648Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of NewsJack BandyNicholas DiakopoulosThis 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.https://doi.org/10.1177/20563051211041648 |
| spellingShingle | Jack Bandy Nicholas Diakopoulos Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News Social Media + Society |
| title | Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News |
| title_full | Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News |
| title_fullStr | Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News |
| title_full_unstemmed | Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News |
| title_short | Curating Quality? How Twitter’s Timeline Algorithm Treats Different Types of News |
| title_sort | curating quality how twitter s timeline algorithm treats different types of news |
| url | https://doi.org/10.1177/20563051211041648 |
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