National Cheng Kung University: Research Institution — Prolific Citations Library

Browse 2 peer-reviewed papers from National Cheng Kung University spanning Social Media and Misinformation Countermeasures in HCI, Human-AI Interaction (2025). Research powered by Prolific's high-quality participant data.

This page lists 2 peer-reviewed papers from researchers at National Cheng Kung University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

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
Do People Think Fast or Slow When Training AI?

Authors: L S. Treiman, CJ Ho, W Kool

Year: 2025

Published in: Proceedings of the 2025 ACM Conference ..., 2025 - dl.acm.org

Institution: Washington University in St. Louis, National Cheng Kung University

Research Area: Human-AI Interaction, Cognitive Science, Behavioral Research in AI Training

Discipline: Human-Computer Interaction (HCI), Behavioral Science

Participants tend to rely on intuition (fast thinking) rather than deliberation (slow thinking) when training AI agents in the ultimatum game, impacting human-AI collaboration system design.

Methods: Participants trained an AI agent in the ultimatum game to analyze whether their training decisions aligned more with intuitive or deliberative cognitive processes.

Key Findings: The cognitive processes (fast vs. slow thinking) underlying human decision-making during AI training.

DOI: https://dl.acm.org/doi/abs/10.1145/3715275.3732177

Citations: 1