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Abstract Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.
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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 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.
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- Award ID(s):
- 1717330
- PAR ID:
- 10547366
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Social Media + Society
- Volume:
- 7
- Issue:
- 3
- ISSN:
- 2056-3051
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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