Experimental Insights into Rumor Spread: Manipulation and Filtering Mechanisms

Last registered on November 07, 2025
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Pre-Trial

Trial Information

General Information

Title
Experimental Insights into Rumor Spread: Manipulation and Filtering Mechanisms
RCT ID
AEARCTR-0016910
Initial registration date
September 30, 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
October 03, 2025, 10:25 AM EDT

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

Last updated
November 07, 2025, 3:55 AM EST

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

Locations

Country
France
Region
Paris

Primary Investigator

Name
liza charroin
Affiliation
Université Paris 1
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
christophe bravard
PI Affiliation

Additional Trial Information

Status
In development
Start date
2025-10-13
End date
2025-12-19
Keywords
Behavior, Lab
Additional Keywords
Networks, rumors.
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This paper investigates how misinformation propagates through social networks using experimental methods that allow us to isolate key mechanisms of message transmission. We focus on the role of individuals in either transferring, blocking, or transforming a message, distinguishing between two treatments: one in which messages can only be transferred or blocked, and another in which individuals can also modify the content before passing it on. These treatments capture real-world dynamics, such as reposting versus quoting on social media, or faithfully relaying versus rephrasing in interpersonal communication.
Our framework departs from classical information cascade models by incorporating biased agents who may manipulate messages, alongside unbiased agents who are incentivized to evaluate credibility and diffuse true information. The central objective is to understand how networks filter information and whether the possibility of manipulation amplifies or mitigates the spread of false rumors. By comparing behavior across treatments, we examine the conditions under which misinformation becomes more or less credible.
External Link(s)

Registration Citation

Citation
bravard, christophe and liza charroin. 2025. "Experimental Insights into Rumor Spread: Manipulation and Filtering Mechanisms." AEA RCT Registry. November 07. https://doi.org/10.1257/rct.16910-1.2
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We design a between-subject experiment with 2 treatments: the No Transformation (NT) treatment and the Transformation (T) treatment.
In each treatment, participants will play the role of an agent in a line-network (cascade) of 6 agents. They may be in position 2, 3 or 4. Agent 1 is the only one who can observe the state of the world (here, a Heads-Tails draw, where Heads only has a 20% chance to be true). Agents in the network are either unbiased (their objective is to find the true state of the world and to diffuse rumors that are credible enough, or biased (their objective is to convince other agents that Heads is true, whatever the true state of the world).
Only the participant is a real agent, the 5 other agents are computerized: this is an individual task. The participant always has the role of an unbiased agent and agents only know the type of their direct friends. We vary the network structure (position of the participant, distribution of biased/unbiased agents, etc.) such that participants face 60 situations. In 42 situations, they receive a message, in 18 they receive nothing because the message was blocked upstream (the situations have been selected before the trial starts to match the probabilities given to participants and to expose them to all the situations possible).
We also designed a theoretical model that predicts what participants should do in each situation (depending on the network and the message they receive).
In both treatments, they have 2 decisions to make:
- Decision 1: based on the network and the message received, they have to guess the state of the world
- Decision 2: they have to decide if they want to transfer, block (or transform in T) the message received.
We link these 2 decisions to avoid incoherent decisions: for instance, if they guess that Heads is the true state of the world, they cannot transfer a Tails message.
Both decisions are incentivized.
In the (18) cases where no message is received, they just have to make Decision 1. In order to shorten the experiment, they will face these 18 situation on one screen at the end of the experiment.
Intervention (Hidden)
In the original design, we planned two parts: one with complete information about the type of each agent, and another where agents only knew the type of their direct neighbors. We ultimately chose to retain only the latter, as the theoretical predictions under "complete information" were highly ambiguous and overly dependent on participants’ beliefs.
Intervention Start Date
2025-11-07
Intervention End Date
2025-12-19

Primary Outcomes

Primary Outcomes (end points)
Two key outcomes:
- Decision 1: in each situation, which state do they think is the true one.
- Decision 2: in each situation, do they transfer or not a message and do they transform it?
Our goal is to see whether the filtering of the network makes messages more credible in one treatment or the other.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We design a between-subject experiment with 2 treatments: the No Transformation (NT) treatment and the Transformation (T) treatment.
In each treatment, participants will play the role of an agent in a line-network (cascade) of 6 agents. They may be in position 2, 3 or 4. Agent 1 is the only one who can observe the state of the world (here, a Heads-Tails draw, where Heads only has a 20% chance to be true). Agents in the network are either unbiased (their objective is to find the true state of the world and to diffuse rumors that are credible enough, or biased (their objective is to convince other agents that Heads is true, whatever the true state of the world).
Only the participant is a real agent, the 5 other agents are computerized. The participant always has the role of an unbiased agent and agents only know the type of their direct friends. We vary the network structure (position of the participant, distribution of biased/unbiased agents, etc.) such that participants face 60 situations. In 42 situations, they receive a message, in 18 they receive nothing because the message was blocked upstream (the situations have been selected before the trial starts to match the probabilities given to participants and to expose them to all the situations possible).
We also designed a theoretical model that predicts what participants should do in each situation (depending on the network and the message they receive).
In both treatments, they have 2 decisions to make:
- Decision 1: based on the network and the message received, they have to guess the state of the world
- Decision 2: they have to decide if they want to transfer, block (or transform in T) the message received.
We link these 2 decisions to avoid incoherent decisions: for instance, if they guess that Heads is the true state of the world, they cannot transfer a Tails message.
Both decisions are incentivized.
In the (18) cases where no message is received, they just have to make Decision 1. In order to shorten the experiment, they will face these 18 situation on one screen at the end of the experiment.
Experimental Design Details
We decided to run 60 periods. Given the parameters chosen (Heads has a 20% chance of being true and 30% of agents are biased), we decided to have 42 periods where a message is received and 18 where no message is received.
Over these 42 periods, participants are 6 times in position 2, 18 times in position 3 and 18 times in position 4 too, because position 2 is less interesting given our theoretical predictions.
In the 42 periods, a Heads signal (sent to agent 1) has a 20% probability approximately to match our parameters.
Randomization Method
The order of the 60 situations has been randomly drawn once, before the trial starts, such that each participant faces the same sequence in both treatments.
Randomization Unit
Each experimental session will randomly be a NT or a T treatment.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
120 participants
Sample size: planned number of observations
120 participants
Sample size (or number of clusters) by treatment arms
60 participants in each treatment
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
Paris School of Economics
IRB Approval Date
2025-07-21
IRB Approval Number
2025-038
Analysis Plan

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