Evidence map›Paper›PMID 40108267›Full record

ArticleScientific reports2025

A path forward on online misinformation mitigation based on current user behavior.

Catherine King, Samantha C Phillips, Kathleen M Carley

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Catherine KingSoftware and Societal Systems Department, Carnegie Mellon University, Pittsburgh, USA. cking2@cs.cmu.edu.
Samantha C PhillipsSoftware and Societal Systems Department, Carnegie Mellon University, Pittsburgh, USA.
Kathleen M CarleySoftware and Societal Systems Department, Carnegie Mellon University, Pittsburgh, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social media misinformation has become a serious societal problem, and recent research has focused on developing effective ways to counter its harmful impacts. This work investigates user-level countermeasures, or how individuals who see the misinformation respond to it directly, possibly to help stop its spread in their online communities. Using a registered report design, we conducted an online survey of 1010 American social media users who use social media at least once weekly. Participants were asked how they respond and think others should respond to misinformation they unintentionally post or see posted by others, and how their responses differ depending on their relationship with the person who posted that misinformation. Overall, the results revealed a difference between respondents' beliefs and actions: participants reported expecting others to exert more effort when responding to misinformation than the level of effort they themselves reported. Additionally, on average, participants were more likely to say they intervened when misinformation was posted by someone close to them rather than by an acquaintance or a stranger. Understanding current behavioral patterns and public opinion can inform efforts to elicit public participation in countering misinformation and increase the effectiveness of platform-level countermeasures.Protocol registration: The stage 1 protocol for this Registered Report was accepted in principle on March 13th, 2024. The protocol, as accepted by the journal, can be found at: https://figshare.com/s/683b1e7c2f2bad96f604 .

Indexed as

CommunicationSocial MediaAdolescentAdultFemaleHumansInternetMaleMiddle AgedPublic OpinionSurveys and QuestionnairesYoung Adult

Identifiers

PMID40108267
PMCPMC11923251

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.