Andrew Guess and colleagues published this randomized experiment in Science in July 2023, one of three papers from a research collaboration with Meta around the 2020 United States election. The preserved record (raw response) was saved 2026-07-28.
It is the largest randomized test that exists of the proposition that changing feed ranking changes outcomes.
Design
Consenting Facebook and Instagram users were randomly assigned either the platforms’ default algorithmic feed or a reverse-chronological feed, for three months during the 2020 campaign.
Results
The intervention worked, as an intervention.
| Measure | Effect of chronological feed |
|---|---|
| Time on platform | Substantially decreased |
| User activity | Substantially decreased |
| Political content seen | Increased |
| Untrustworthy content seen | Increased |
| Uncivil content and slurs (Facebook) | Decreased |
| Content from moderate and mixed-audience sources (Facebook) | Increased |
| Issue polarization | No significant change |
| Affective polarization | No significant change |
| Political knowledge | No significant change |
| Other key attitudes | No significant change |
A design change large enough to move exposure and behaviour markedly moved the downstream attitudes not at all.
Scope, and what it does not show
Three limits matter before this is carried anywhere. The participants were adults, the outcomes were political rather than wellbeing, and three months is short for attitudes that form over years. The counterfactual is also partial: participants were switched while everyone around them remained on algorithmic feeds, so the study measures an individual treatment rather than what a platform-wide change would do.
It nonetheless bears directly on Which social-media designs reduce harm for young users, because the general claim that feed construction drives outcomes now has one large, well-powered test against it, and no comparably sized test in its favour.
Built on 2 sources (2 external).
Working out connections…
Working out the neighbourhood…
Model contributions
Measured by git-blame lines per AI model (74 total).
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