Algorithms and Parasitic Content on Social Media

Last registered on January 05, 2026
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Pre-Trial

Trial Information

General Information

Title
Algorithms and Parasitic Content on Social Media
RCT ID
AEARCTR-0017457
Initial registration date
December 22, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
January 05, 2026, 7:13 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Nancy Wang
Affiliation
MIT
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Other Primary Investigator(s)

PI Name
Hannah Solheim
PI Affiliation
Columbia University
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Additional Trial Information

Status
In development
Start date
2025-12-22
End date
2026-06-30
Keywords
Behavior, Lab, Welfare
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
We conduct a lab experiment in which we study how social media users interact with social media content and how it in turn affects their well-being.
External Link(s)

Registration Citation

Citation
Solheim, Hannah and Nancy Wang. 2026. "Algorithms and Parasitic Content on Social Media." AEA RCT Registry. January 05. https://doi.org/10.1257/rct.17457-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-12-22
Intervention End Date
2026-06-30

Primary Outcomes

Primary Outcomes (end points)
Primary outcomes include incentivized stated preferences for different types of content, watch behavior, and measures of well-being (i.e., body appreciation, open-ended text responses, mood).

More detail is provided in the attached document.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes include first stage measures of the feed (e.g., if the participants thought the feed was uplifting, what the participants report seeing).

More detail is provided in the attached document.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will have three treatment arms and one control arm with randomization at the individual level. This is a one-shot experiment.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual. Furthermore, once randomized into a treatment arm, the individual will receive videos that are randomly drawn from a distribution of videos, where the distribution is determined by the treatment arm.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
At least 1000
Sample size (or number of clusters) by treatment arms
We plan to randomize at least 1000 participants in fixed proportions: 55% in Treatment 1, 15% in Treatment 2, 15% in Treatment 3, and 15% in Control. We may change the sample size after hearing results from grant applications.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Given our current design and assuming 85% take-up, we calculate the following MDEs, reported in standard errors, for our continuous main outcomes. We plan for our main analyses to be between treatments 2 or 3 vs. control, or behavior in treatment 1 by baseline characteristics (e.g., stated preferences). • Treatments 2 or 3 vs. Control: 0.2 SD • Treatment 1 vs. Stated Preferences (assuming a 50/50 split): 0.26 SD
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
MIT Committee on the Use of Humans as Experimental Subjects
IRB Approval Date
2025-12-18
IRB Approval Number
E-7374
Analysis Plan

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