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Affective Polarization during Electoral Times

Last registered on August 29, 2022
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Pre-Trial

Trial Information

General Information

Title
Affective Polarization during Electoral Times
RCT ID
AEARCTR-0009982
Initial registration date
August 26, 2022

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 29, 2022, 2:19 PM EDT

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

Locations

Country
Brazil
Region

Primary Investigator

Name
Pedro Sant'Anna
Affiliation
São Paulo School of Economics Fundação Getulio Vargas
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Other Primary Investigator(s)

PI Name
Bruno Ferman
PI Affiliation
São Paulo School of Economics Fundação Getulio Vargas
Contact Investigator
PI Name
Nicolás Ajzenman
PI Affiliation
McGill University
Contact Investigator

Additional Trial Information

Status
On going
Start date
2022-07-12
End date
2023-02-28
Keywords
Behavior, Electoral
Additional Keywords
JEL code(s)
C93; D72; D91; Z20
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We study the interplay between political and affective congruence in the formation of social ties on Twitter, in the context of the Brazilian 2022 elections.
External Link(s)

Registration Citation

Citation
Ajzenman, Nicolás, Bruno Ferman and Pedro Sant'Anna. 2022. "Affective Polarization during Electoral Times." AEA RCT Registry. August 29. https://doi.org/10.1257/rct.9982-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
We create fictitious bot accounts on Twitter that signal two dimensions of identity: first, political preference for one of the two main political candidates in the 2022 Brazilian presidential elections (Luiz Inacio Lula da Silva or Jair Messias Bolsonaro); second, preference for a Brazilian soccer club, which we interpret as a dimension of identity that is traditionally affective. The bot accounts randomly follow Twitter accounts with congruent and incongruent identities across these two dimensions. We compute the proportion of follow-backs and blocks received by the
bots as measures of the formation of social ties. We run the experiment in the second semester of 2022, both before and after the Brazilian presidential election.
Intervention Start Date
2022-07-12
Intervention End Date
2022-12-22

Primary Outcomes

Primary Outcomes (end points)
See pre-analysis plan.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
See pre-analysis plan.
Experimental Design Details
The experiment will consist of the creation of fictitious accounts on Twitter that signal their preferred candidate in the 2022 Brazilian election (Luis Inácio Lula da Silva or Jair Messias Bolsonaro) and their preferred Brazilian soccer club. The bot accounts randomly follow Twitter users who share or not each identity (political and soccer club preference) with it. After five days of activation, we compute the number of follow-backs and blocks obtained by each bot. The experiment will be run in waves during the second semester of 2022. Overall, we plan on running 44 experimental waves between July and December, 2022.

The subject pool is composed of Twitter accounts from Brazilians who tweeted or re-tweeted a status containing a pro-Lula or pro-Bolsonaro hashtag between May and July 2022. From these accounts, we identify the ones that also signal a preferred soccer club in their profile. This is our final subject pool.

In each experimental wave (with a time span of 5 days), we will activate 8 bot accounts that differ in their preferred soccer club and/or political candidate. Each account will randomly follow approximately 100 accounts from the subject pool. We perform block-randomization to determine treatment assignment, stratifying by subject's political and affective identity, as well as by whether the subject is above or below the sample's median number of followers. Each subject may be treated (i.e., followed by a bot) more than once, but never in subsequent waves: after being treated in a wave, a subject only returns to the subject pool after 3 waves.
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual (Twitter user)
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
352 accounts
Sample size: planned number of observations
35,000 subjects (5,000 individuals who may be treated more than once in different treatment waves)
Sample size (or number of clusters) by treatment arms
4,400 subjects
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethical Compliance Committee on Research Involving Human Beings of Fundaçao Getulio Vargas
IRB Approval Date
2022-06-29
IRB Approval Number
Opinion n. 208/2022
Analysis Plan

Analysis Plan Documents

PAP_PolarizationTwitter_Aug26.pdf

MD5: b95517de6f0aed2c42dede9f5ac022c6

SHA1: c502fe7d2e620cff368d37899212f192aaf07155

Uploaded At: August 26, 2022

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