Social Media, Well-being, and Political Attitudes: The Causal Effects of Recommender Algorithms

Last registered on June 06, 2023

Pre-Trial

Trial Information

General Information

Title
Social Media, Well-being, and Political Attitudes: The Causal Effects of Recommender Algorithms
RCT ID
AEARCTR-0011464
Initial registration date
May 22, 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
June 06, 2023, 9:09 AM EDT

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

Locations

Primary Investigator

Affiliation
University of St.Gallen

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2023-05-22
End date
2023-08-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We study the causal effects of social media recommender algorithms on well-being and political opinions, specifically on Twitter.
External Link(s)

Registration Citation

Citation
Hodler, Roland. 2023. "Social Media, Well-being, and Political Attitudes: The Causal Effects of Recommender Algorithms ." AEA RCT Registry. June 06. https://doi.org/10.1257/rct.11464-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2023-05-22
Intervention End Date
2023-08-31

Primary Outcomes

Primary Outcomes (end points)
The subjects' Twitter activity, well-being, and political attitudes (regarding political orientation and current events, e.g., the current situation in Ukraine). Variables are constructed based on our pre- and post-treatment surveys and the subjects' publicly accessible Twitter activities.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Each subject is randomly assigned to one of two groups at the end of the pre-treatment survey. Subjects in the treatment group are asked to use Twitter's algorithmic newsfeed setting; those in the control group to use the chronological newsfeed setting. All subjects are asked to use only this one newsfeed setting for six weeks. We then conduct our post-treatment survey.
Experimental Design Details
Each subject is randomly assigned to one of two groups at the end of a pre-treatment survey. Subjects in the treatment group are asked to use Twitter's algorithmic newsfeed setting; those in the control group to use the chronological newsfeed setting. All subjects are asked to use only this one newsfeed setting for six weeks. We then conduct our post-treatment survey.
In addition, we let some subjects run a Google Chrome extension (on their own computers) to collect what they would see under the two different newsfeed settings. Moreover, we collect information on all the subjects' Twitter activity (tweets, retweets, their profile description, liked tweets, who they follow, and who follows them). The data on their Twitter activity are publicly available; we can access them since the subjects indicate their Twitter username in the survey (for this purpose).
Randomization Method
Randomization done by a computer program in Swiss student sample. Stratified randomization done by survey company in US population sample.
Randomization Unit
Individuals
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clustering
Sample size: planned number of observations
For the Swiss student sample, we invite all students of a large introductory economics lecture to participate in our survey experiment. We expect around 100 students to participate. For the US population sample, we target around 6,000 subjects who are U.S. residents, Twitter users, and at least 18 years old.
Sample size (or number of clusters) by treatment arms
For the Swiss student sample, we expect around 50 students per treatment arm.
For the US population sample, we expect around 3,000 subjects per treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethics committee of the University of St.Gallen
IRB Approval Date
2023-04-05
IRB Approval Number
HSG-EC-20230223

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials