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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
Last updated
September 01, 2020 11:40 AM EDT
Location(s)

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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.6014-1.2000000000000002.
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
Not available
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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