Understanding online influence: Experimental evidence from the immigration debate on social media

Last registered on December 06, 2021

Pre-Trial

Trial Information

General Information

Title
Understanding online influence: Experimental evidence from the immigration debate on social media
RCT ID
AEARCTR-0008381
Initial registration date
October 15, 2021
Last updated
December 06, 2021, 10:23 AM EST

Locations

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Primary Investigator

Affiliation
University College Dublin

Other Primary Investigator(s)

PI Affiliation
PI Affiliation
PI Affiliation
PI Affiliation

Additional Trial Information

Status
In development
Start date
2021-10-18
End date
2022-06-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project proposes to study the effect of social media on individuals' political attitudes through an online survey experiment. Our experiment is designed to disentangle some of the precise mechanisms through which online interactions lead to political influence. Our focus is on the most common feature of social media interactions: likes. Can the number of likes we see on social media messages affect its influence on us? Does liking a post make us more persuaded by the post's content? Does the salience of other participants' engagement matter? Does the fact that our likes are shown to others matter?

To answer these questions, we will conduct an online survey experiment in the context of the migration debate in the EU. We will recruit participants from four EU countries, Ireland, Italy, Hungary and France, which vary widely in their attitudes towards immigration.
External Link(s)

Registration Citation

Citation
Conzo, Pierluigi et al. 2021. "Understanding online influence: Experimental evidence from the immigration debate on social media." AEA RCT Registry. December 06. https://doi.org/10.1257/rct.8381-1.6000000000000005
Experimental Details

Interventions

Intervention(s)
Subjects are shown 6 messages, formatted to look like social media posts, one at a time. Posts 2, 4, and 6 are randomly selected from tweets by EU members of parliament (with minor rewording for clarity) that have been pre-tested on another set of participants in a different study. Based on this pre-test we order posts from most pro- to most anti-immigration. The number of likes for each text will be randomised from two uniform distributions: low [1,50] or high [151,200]. For posts 1, 3 and 5, subjects are shown placebo texts.

Subjects are randomised into six treatments that vary in two dimensions. First, subjects are either told that the posts "have been liked by previous participants" or not. Second, we vary the ability to like the posts: in one condition subjects are not given the possibility to like, in another subjects can like privately, and in a third condition subjects can like publicly---thereby disentangling the effect of liking per se and the visibility of the like.
Intervention Start Date
2021-12-01
Intervention End Date
2022-01-31

Primary Outcomes

Primary Outcomes (end points)
i) Participants' responses to immigration questions taken from the ESS. We will use a linear combination of the responses as our measure of post-treatment attitude. We define larger magnitudes as being more anti-immigration.
ii) Donation to a pro-immigration charity.
iii) Intention to sign a pro-immigration petition.
iv) Report of signing the petition.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We first measure participants' attitudes towards immigration using a random subset of questions from the ESS.

We then randomise them into 1 of the 6 treatments described above, where they are shown 6 posts (3 on immigration, 3 placebos) with varying number of likes.

We then measure their post-treatment attitudes using the remaining ESS questions, elicit their willingness to donate to the pro-immigration charity and their willingness to sign a pro-immigration petition.

We finish with an attention check (asking participants if they remember how many likes they observed on 2 of the posts) and their trust in a randomly selected post.
Experimental Design Details
Not available
Randomization Method
Randomisation is done through the experimental software.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
3000 individuals: 1000 in France and Italy, 500 in Ireland and Hungary
Sample size: planned number of observations
3000 individuals: 1000 in France and Italy, 500 in Ireland and Hungary
Sample size (or number of clusters) by treatment arms
500 in each of the 6 treatments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University College Dublin
IRB Approval Date
2021-05-26
IRB Approval Number
HS-E-20-110-Samahita
Analysis Plan

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