Algorithmic News Feeds, Personalization, and Democratic Outcomes: Evidence from an App Patient-Preferred Field Experiment in Italy

Last registered on September 01, 2020

Pre-Trial

Trial Information

General Information

Title
Algorithmic News Feeds, Personalization, and Democratic Outcomes: Evidence from an App Patient-Preferred Field Experiment in Italy
RCT ID
AEARCTR-0006014
Initial registration date
August 03, 2020

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
August 05, 2020, 10:12 AM EDT

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

Last updated
September 01, 2020, 11:40 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
Stanford University

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2020-08-10
End date
2020-09-30
Secondary IDs
Abstract
Algorithmic news feeds provide a combination of news stories with a very high degree of personalization. Personalized political information may affect democratic outcomes by how it bolsters false consensus beliefs, and that, in contexts of high levels of polarization, may create an artificial wedge between political parties. This paper investigates the implications of personalized news feeds, by implementing a pre-registered, globally replicable, lab-in-the-field experiment, in which subjects are treated with a custom-developed news app, and then evaluated with surveys in the changes to their political and democratic beliefs. The experiment is a patient-preferred trial to explicitly account for the impact of self-selection. Results from the pilot show that algorithmic news feed are quite powerful in determining media diets, but ineffective at changing preferences.
External Link(s)

Registration Citation

Citation
Vecchiato, Alessandro. 2020. "Algorithmic News Feeds, Personalization, and Democratic Outcomes: Evidence from an App Patient-Preferred Field Experiment in Italy." AEA RCT Registry. September 01. https://doi.org/10.1257/rct.6014
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
This intervention requires participants to download a news-feed app that collects real news headlines from all national newspapers in Italy. Subjects are asked to use the app for a period of 15 days. The app provides two manipulations, including a chronological and a personalized news-feed. Outcomes will be evaluated on a number of politically relevant variables, including voting intentions and political knowledge.
Intervention Start Date
2020-08-20
Intervention End Date
2020-09-30

Primary Outcomes

Primary Outcomes (end points)
The study includes behavioral and surveys outcomes. The app will collect information on users' behavior online, including reading time, clicks, and number, and length of sessions. Survey outcomes include political preferences, political knowledge, and second-order beliefs on others.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment includes two treatment conditions (popular and personalized news feeds) and one control condition (no app). The experimental protocol includes two treatment arms, one with self-selection into treatment and one with randomized treatment. This design will allow the experimenter to develop estimates of the likelihood of users taking up treatment, and therefore estimate the outcome averages were the treatment be implemented outside of the experimental setting.
Experimental Design Details
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Treatment is assigned at the participant level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
The subject pool is of 700 users.
Sample size: planned number of observations
700 observations.
Sample size (or number of clusters) by treatment arms
250 control, 550 treatment (50% per treatment arm).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The minimum detectable effect is of 0.2 standard deviations of the control group for each dependent variable.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Panel on Human Subjects in Non-Medical Research
IRB Approval Date
2020-12-06
IRB Approval Number
54956
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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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