Rumors in social networks

Last registered on October 15, 2021

View Trial History

Pre-Trial

Trial Information

General Information

Title

Rumors in social networks

RCT ID

AEARCTR-0008369

Initial registration date

October 14, 2021

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 15, 2021, 4:28 PM EDT

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

Locations

Country

France

Region

Primary Investigator

Name

liza charroin

Affiliation

Université Paris 1

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Francis Bloch

PI Affiliation

PI Name

Sudipta Sarangi

PI Affiliation

Additional Trial Information

Status

In development

Start date

2021-10-18

End date

2022-03-31

Keywords

Behavior

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

In this online lab-experiment, we study the diffusion of rumors (uncertain information with no clear information on its source) in a social network.
During the experiment, groups of 5 agents are structured in a line network. Some agents (4 out of 5 or 3 out of 5, depending on the treatment) are computerized agents, programmed to behave optimally. The other agents are real participants.
One of the agents in the network receives a signal after a coin flip (Heads or Tails). Heads happens with a probability 0.3, while Tails happens with probability 0.7. This agent can create a message ("The outcome of the coin flip is Heads" or "The outcome of the coin flip is Tails") and transfer it to his direct neighbor(s). The message may then be diffused through the network. More precisely, each agent receiving the message may decide to transfer or block the message. At the end of the period, the group votes for a specific outcome (the outcome implemented by the group depends on a probability function).
There are two types of agents: biased and unbiased agents, with different payoff functions. Biased agents earn points only if the members of the network have implemented thanks to their votes the outcome "Heads", whatever the true outcome of the coin flip. Unbiased agents earn points if the outcome implemented by the group corresponds to the outcome of the coin flip. So while unbiased agents are willing to match the true outcome, biased agents try to enforce "Heads" by influencing the network.
The game is repeated 48 times (varying distribution and location of biased and unbiased agents).
We design two treatments: in the first treatment, only one agents out of five is a real participant, while in the second treatment, two agents out of five are real participants. The objective is to understand the social/human component in the spreading of rumors.

External Link(s)

Registration Citation

Citation

Bloch, Francis, liza charroin and Sudipta Sarangi. 2021. "Rumors in social networks." AEA RCT Registry. October 15. https://doi.org/10.1257/rct.8369-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2021-10-18

Intervention End Date

2021-11-26

Primary Outcomes

Primary Outcomes (end points)

Decision to transfer or block a message (if they receive a message).
Decision to vote for Heads or Tails (if they did not receive any message).

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Decision time.

Secondary Outcomes (explanation)

Experimental Design

- Online experiment, using the online platform of the University.
- Two treatments: one treatment with one real participant, the second one with two real participants per groups of 5 agents.
- Some agents are biased and others are unbiased. Our real participants are always unbiased.
- In each period, one (computerized) agent receives a signal (Head or Tails, Heads has a probability 0.3) and can then create a (truthful or untruthful) message about the signal received and send it to his direct neighbor(s). The agent(s) who received the message can transfer or block the message. The communication phase ends when every agent received the message or when the message is no longer transferred.
Information: agents do not know the identity of the source of the message but they know where biased/unbiased agents are located in the network (they know their own position too) and they know from which part of the network the message comes from. Thus, they can infer the probability of the veracity of the message.
- At the end of the period, agents vote for their preferred outcome. Based on a probability function, the outcome implemented by the group is either Heads or Tails; the more agents vote for Heads (resp. Tails), the higher the likelihood of Heads (resp. Tails).
- Unbiased agents earn 6 points if the outcome implemented by the group matches the signal (0 point otherwise), while biased agents earn 6 points if the outcome implemented by the group is Heads (0 point otherwise).
- Process of voting: in order to avoid participants beliefs to differ from their transfer/block decisions, the decision to transfer a Heads-message corresponds to a Heads-vote (same for Tails), while the decision to block a Heads-message (resp. Tails-message) corresponds to a Tails-vote (resp. Heads-message).
- In each treatment, we test 48 different networks (varying the number and position of biased/unbiased agents) corresponding to 48 different periods. Our goal is to test whether participants are able to diffuse and infer the veracity of messages with different possible networks.

Experimental Design Details

Randomization Method

Randomization of the order of the networks was made once for all the sessions (all participants faced the same random order).
We also randomized the pair of players in the second treatment (partner-matching).
The sessions of the first treatment will be run first and then we will run the sessions for the second treatment.

Randomization Unit

Randomization at the network level (one random order made before the beginning of all the sessions).
Random pairs will be formed in the second treatment (at the individual level).
The sessions of the first treatment will be run first and then we will run the sessions for the second treatment.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

- Around 110 participants in the first treatment (main treatment): individual decisions.
- Around 60 participants in the second treatment (extension of the main treatment): pairs of 2, so 30 clusters.

Sample size: planned number of observations

Around 170 participants.

Sample size (or number of clusters) by treatment arms

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

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