Misinformation Countermeasures: Research Area — Prolific Citations Library

Discover 2 peer-reviewed studies in Misinformation Countermeasures (2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 2 peer-reviewed papers in the research area of Misinformation Countermeasures in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

What's in the black box? How algorithmic knowledge promotes corrective and restrictive actions to counter misinformation in the USA, the UK, South Korea and Mexico

Authors: M Chung

Year: 2025

Published in: Internet Research, 2023 - emerald.com

Institution: University of Washington, Emory University

Research Area: Algorithmic Knowledge, Misinformation Countermeasures, Comparative Media Studies, Information Science

Discipline: Information Science

The study examines how algorithmic knowledge influences attitudes and actions against misinformation, revealing that perceptions of media influence on self and others predict corrective actions and support for regulation differently across four countries.

Methods: Four national surveys were conducted in the USA, UK, South Korea, and Mexico, with data analyzed through multigroup structural equation modeling (SEM).

Key Findings: Algorithmic knowledge, perceived influence of misinformation on self and others, intention to correct misinformation, support for regulation and content moderation.

DOI: https://doi.org/10.1108/INTR-07-2022-0578

Citations: 14

Sample Size: 5432
Community notes vs. related articles: Assessing real-world integrated counter-rumor features in response to different rumor types on social media

Authors: S Kankham, JR Hou

Year: 2025

Published in: International Journal of Human--Computer ..., 2025 - Taylor & Francis

Institution: National Cheng Kung University

Research Area: Social Media and Misinformation Countermeasures in HCI

Discipline: Human-Computer Interaction (HCI)

The study found that integrated counter-rumor features, such as community notes and related articles, reduce users' intentions to believe and spread social media rumors; community notes worked better for 'wish' rumors, while related articles were more effective for 'dread' rumors.

Methods: Conducted an experimental study evaluating the effects of community notes and related articles on online users' intentions to believe and spread two types of rumor tweets: wish and dread rumors.

Key Findings: Online users' intentions to believe and spread rumors on social media with and without integrated counter-rumor features (community notes and related articles).

DOI: https://www.tandfonline.com/doi/abs/10.1080/10447318.2024.2400389

Citations: 11

Sample Size: 201