Can influencer be influenced on social media: evidence from a field experiment

Last registered on January 14, 2026
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Pre-Trial

Trial Information

General Information

Title
Can influencer be influenced on social media: evidence from a field experiment
RCT ID
AEARCTR-0017495
Initial registration date
December 19, 2025

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
January 05, 2026, 6:59 AM EST

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

Last updated
January 14, 2026, 5:32 AM EST

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

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Primary Investigator

Name
Zhiyu Liu
Affiliation
The Chinese University of Hong Kong, Shenzhen
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Other Primary Investigator(s)

PI Name
Tianyu Zhang
PI Affiliation
The Chinese University of Hong Kong, Shenzhen
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PI Name
Susan Shu
PI Affiliation
Boston College
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PI Name
Zihao Cheng
PI Affiliation
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Additional Trial Information

Status
On going
Start date
2025-06-01
End date
2026-03-31
Keywords
Finance & Microfinance
Additional Keywords
Key Opinion Leaders; Opinion Diversity
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this study, we examine how dissenting opinions to that of key opinions leaders (KOL) influence the information environment on an investor-focused online platform. Prior research has highlighted the role of social media in shaping information environments. However, the communication online can easily to lead to an echoing chamber, where the opinions of KOL are exaggerated by his/her followers in the absence of critical or scientific check. We will investigate whether the dissenting comments from the audience can influence the behavior of influencers, the KOLs. To test this, we design a field experiment on an investor social platform, where standardized dissenting and supporting comments will be posted as replies to opinions of a sample of randomly selected KOLs. To avoid subjective bias by human, we will rely on AI to generate the comments with different position right after the KOL publish the post. We will perform several sets of analyses: 1) How the KOL responds to our comments; 2) How the posts of KOLs will change; 3) How the interaction between among the audience will change; 4) Whether the information quality of KOLs’ posts improve. We expect to find that dissent acts as a subtle but effective monitoring mechanism for information providers, improving effectiveness of communication and enhancing information quality in the social media.
External Link(s)

Registration Citation

Citation
Cheng, Zihao et al. 2026. "Can influencer be influenced on social media: evidence from a field experiment." AEA RCT Registry. January 14. https://doi.org/10.1257/rct.17495-1.2
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-11-15
Intervention End Date
2026-01-14

Primary Outcomes

Primary Outcomes (end points)
1) How the KOLs respond to our comments; 2) How the posting behavior of KOLs will change; 3) How the interaction among the audience will change; 4) Whether the information quality of KOLs’ posts improve
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In the experimental design, a sample of influential KOL accounts is randomly assigned to one of four groups, each of which receives instant, AI-generated comments following their new post. The first group receives dissenting comments that logically refute the viewpoint expressed in each new post. The second group receives supporting comments that reinforce the original opinion. The third group receives neutral comments, which are unrelated to the stance of the KOL but maintain a non-confrontational and context-relevant tone. And the fourth group serves as the control, receiving no comments, providing a baseline for comparison.
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
key opinion leader accounts
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
The initial sample includes 1,292 opinion leader accounts, though the final sample may be smaller due to account deactivation during the experimental period. In addition, the study includes an auxiliary sample of 435 low-attention accounts that are exposed to the intervention as part of an additional analysis beginning on December 4, 2025.
Sample size: planned number of observations
The study is expected to include approximately 100,000 to 200,000 posts, though the exact number of observations cannot be predetermined.
Sample size (or number of clusters) by treatment arms
Main sample:
322 accounts control, 324 accounts dissenting comment group, 324 accounts supporting comment group, 322 accounts neutral comment group
Low-attention sample:
108 accounts control, 108 accounts dissenting comment group, 111 accounts supporting comment group, 108 accounts neutral comment group
(the final sample may be smaller)

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
The Chinese University of Hong Kong (Shenzhen) Institutional Review Board
IRB Approval Date
2025-06-05
IRB Approval Number
CUHKSZ-D-20250047
Analysis Plan