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Political Polarization and Social Media
Last registered on October 15, 2019

Pre-Trial

Trial Information
General Information
Title
Political Polarization and Social Media
RCT ID
AEARCTR-0004850
Initial registration date
October 12, 2019
Last updated
October 15, 2019 9:54 AM EDT
Location(s)

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Primary Investigator
Affiliation
Universidad Torcuato Di Tella
Other Primary Investigator(s)
PI Affiliation
Universidad Torcuato Di Tella
PI Affiliation
Harvard University
Additional Trial Information
Status
In development
Start date
2019-09-20
End date
2020-12-31
Secondary IDs
Abstract
A widespread concern exists regarding social media contributing to political polarization. Social media sites, by promoting the creation of “echo chambers” (situations in which beliefs are amplified or reinforced by communication and repetition inside closed systems), are believed to distort the salience of some beliefs and perceptions, potentially leading to polarized views. However, given that signing-up in a social network is not mandatory, a valid argument is that the presence of polarization patterns documented in social networks may arise in part because of their users being particularly prone to polarization. In other words, patterns observed in social networks may be simply the consequence of self-selection. Disentangling the magnitudes of these effects --polarization effectively caused by exposure to social media versus selection bias-- remains an open question.

We will conduct an experiment with participants from two populations: participants with and without Twitter accounts. Participants will randomly watch either a live stream of Argentina’s 2019 presidential debate or a placebo video. Also randomly, during the debate Twitter users will be asked to turn off their phones, to use their Twitter accounts as they wish, or to post content on their Twitter accounts. In this way, we plan to measure and compare two things. First, the way in which different degrees of access to social media affect polarization indices and stress levels. Second, the differential reactions on these variables for Twitter and non-Twitter users. This evidence will help to disentangle the roles of exposure and selection in the polarization observed in social media.
External Link(s)
Registration Citation
Citation
Di Tella, Rafael, Ramiro Galvez and Ernesto Schargrodsky. 2019. "Political Polarization and Social Media." AEA RCT Registry. October 15. https://doi.org/10.1257/rct.4850-1.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
To avoid experimenter demand effects, the study is described in detail in fields that will not become public until after the trial end date.
Intervention Start Date
2019-10-13
Intervention End Date
2020-03-31
Primary Outcomes
Primary Outcomes (end points)
(1) Differences in measures of political polarization of participants across treatment arms.
(2) Differences in participants’ positive and negative perceptions of politicians across treatment arms.
(3) Differences in participants’ measures of affective polarization, institutional views, and political preferences of participants across treatment arms.
(4) Differences in salivary cortisol levels of participants across treatment arms.
(5) Heterogeneous effects across pre-treatment sociodemographic characteristics, ideology and beliefs.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
To avoid experimenter demand effects, the study is described in detail in fields that will not become public until after the trial end date.
Experimental Design Details
Not available
Randomization Method
As participants arrive to the University premises, Twitter users will be instructed to go to a counter and non-Twitter users to go to another counter. Then, Twitter and non-Twitter users will be separately randomized into placebo and treatments.
Randomization will be made by order of appearance in the following way:
- Using an office computer, we will randomize whether the first arrived non-Twitter user will be assigned to the placebo group, or to treatment group 1. The following one will be assigned to the other group, and so on.
- Using an office computer, we will randomize the order of assignment of Twitter users into the placebo group, the treatment group 1, the treatment group 2, and the treatment group 3. Then, we will use that sequence to assign Twitter participants by their order of arrival..
Randomization Unit
Participants
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
We aim to have about 540 participants (360 Twitter users and 180 non-Twitter users)
Sample size: planned number of observations
We aim to have about 540 participants (360 Twitter users and 180 non-Twitter users).
Sample size (or number of clusters) by treatment arms
1) Non-Twitter users in placebo group: about 90 participants (one half of non-Twitter participants).
2) Non-Twitter users in treatment group 1: about 90 participants (one half of non-Twitter participants).
3) Twitter users in placebo group: about 90 participants (one quarter of Twitter participants).
4) Twitter users in treatment group 1: about 90 participants (one quarter of Twitter participants).
5) Twitter users in treatment group 2: about 90 participants (one quarter of Twitter participants).
6) Twitter users in treatment group 3: about 90 participants (one quarter of Twitter participants).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Comité Institucional de Ética - Instituto de Neurología Cognitiva (INECO), Buenos Aires, Argentina
IRB Approval Date
2019-09-17
IRB Approval Number
N/A
Analysis Plan

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