Algorithmic Drivers of Behavior on Social Media

Last registered on February 13, 2023


Trial Information

General Information

Algorithmic Drivers of Behavior on Social Media
Initial registration date
February 10, 2023

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
February 13, 2023, 10:48 AM EST

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



Primary Investigator

Brown University

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Social media algorithms are an increasing part of our everyday lives, yet little is know about the causal effect of these algorithms on individual well-being. In this project, I study the effect of algorithms on consumer surplus and social welfare. This is done by disentangling the effects of user preferences for and attitudes towards different types of content, from the effects of algorithmic amplification of such content in a large-scale experiment, in cooperation with one of India’s largest social media platforms. I study the causal effect of these algorithms on user engagement with content on the platform, as well as on survey outcomes including users' subjective well-being, and willingness to pay for content customization via algorithms.
External Link(s)

Registration Citation

Kalra, Aarushi. 2023. "Algorithmic Drivers of Behavior on Social Media." AEA RCT Registry. February 13.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
User engagement by content type, defined by genre, hash tags and text analysis of posts. Off-platform outcomes are collected through the survey, and these include, subjective well-being and mental health, digital addiction, attitudes towards out-group members, user perception about changes in content exposure.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The control group consists of a random sample of users on a social media platform who are exposed to a ranked list of posts, where the ranking is determined by the user preferences as they are revealed in their previous engagement and are learnt by the algorithm. Treated users are shown a list of content which is not ranked according to user preferences but are instead exposed to posts that are randomly drawn from a set of 'candidate’ posts. I generate the treatment arms to construct appropriate counterfactuals to algorithmic customization, with varying degrees of content customization by altering different dimensions of the recommendation system.
Experimental Design Details
Randomization Method
Random number generator on Google Cloud Compute.
Randomization Unit
neighborhood level in urban areas, and village level in rural areas.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
20,000 neighborhoods.
Sample size: planned number of observations
4 million users.
Sample size (or number of clusters) by treatment arms
10,000 neighborhoods in treatment and 10,000 neighborhoods in control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials