M Chu: Researcher — Prolific Citations Library

Explore 4 peer-reviewed studies by M Chu in Algorithmic Knowledge and Misinformation Countermeasures (2022–2025). Discover research powered by Prolific's participant panel.

This page lists 4 peer-reviewed papers authored or co-authored by M Chu in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (4 of 4)

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
Online images amplify gender bias

Authors: D Guilbeault, S Delecourt, T Hull, BS Desikan, M Chu

Year: 2024

Published in: Nature, 2024 - nature.com

Institution: University of California Berkeley, Institute For Public Policy Research, Columbia University, University of Southern California Los Angeles

Research Area: Gender Bias, Computational Social Science, Online Media, AI Bias

Discipline: Computational Social Science

Online images significantly amplify gender bias compared to text, with biases in visual content impacting societal beliefs about gender roles.

Methods: Analyzed 3,495 social categories using over one million images from platforms like Google, Wikipedia, and IMDb, compared visual content to billions of words from the same platforms, and conducted a preregistered national experiment to assess the psychological impact on participants' beliefs.

Key Findings: The prevalence and psychological impact of gender bias in online images compared to text, including gender associations and representation disparities.

DOI: https://doi.org/10.1038/s41586-024-07068-x

Citations: 72

Sample Size: 3495
Share to stop the harm: How social media metrics drive sharing of fact-checking messages via first-person perception

Authors: M Chung

Year: 2024

Published in: Mass Communication and Society, 2024 - Taylor & Francis

Institution: Northeastern University

Research Area: Misinformation, Fact-Checking, Social Media Behavior

Discipline: Social Science

DOI: https://www.tandfonline.com/doi/abs/10.1080/15205436.2023.2240302

Citations: 5
Social media regulation, third-person effect, and public views: A comparative study of the United States, the United Kingdom, South Korea, and Mexico

Authors: M Chung, J Wihbey

Year: 2022

Published in: New Media & Society, 2024 - journals.sagepub.com

Research Area: Communication and Media Studies, Political Science, or International Relations (Comparative Studies)

Discipline: Social Sciences, Humanities

DOI: https://doi.org/10.1177/14614448221122996

Citations: 24