@article{ART003215425},
author={Heejun Kim and Bogeum Choi and Jaime Arguello},
title={Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge},
journal={Journal of the Korean Biblia Society for Library and Information Science},
issn={1229-2435},
year={2025},
volume={36},
number={2},
pages={183-209}
TY - JOUR
AU - Heejun Kim
AU - Bogeum Choi
AU - Jaime Arguello
TI - Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge
JO - Journal of the Korean Biblia Society for Library and Information Science
PY - 2025
VL - 36
IS - 2
PB - Journal Of The Korean Biblia Society For Library And Information Science
SP - 183
EP - 209
SN - 1229-2435
AB - Assessing the credibility of online health information has become increasingly complex as the volume of user-generated content (UGC) increases. This study investigates the predictive modeling of credibility in two distinct types of UGC platforms—Yahoo! Answers and Yelp—by exploring the impact of feature categories and the role of assessors’ prior knowledge. A total of 2,000 labeled instances were collected through crowdsourcing, using a rigorously validated credibility instrument and qualification process. Eighty-four features were developed and grouped into categories informed by the Elaboration Likelihood Model (ELM), and feature ablation studies were conducted independently on both datasets. Results indicate that content informativeness was the most discriminative factor for Yahoo! Answers, while sentiment and content informativeness were significant for Yelp. Interestingly, prior knowledge had a platform-dependent effect: it reduced model performance in Yahoo! Answers, likely due to overconfidence and limited domain expertise, but improved performance in Yelp, where lived experience aligned with subjective content. These findings emphasize the importance of tailoring credibility assessments and feature sets to the type of platform and the nature of the content.
KW - Credibility;Health Information;User-Generated Content;Prior Knowledge;Machine Learning
DO -
UR -
ER -
Heejun Kim, Bogeum Choi and Jaime Arguello. (2025). Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge. Journal of the Korean Biblia Society for Library and Information Science, 36(2), 183-209.
Heejun Kim, Bogeum Choi and Jaime Arguello. 2025, "Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge", Journal of the Korean Biblia Society for Library and Information Science, vol.36, no.2 pp.183-209.
Heejun Kim, Bogeum Choi, Jaime Arguello "Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge" Journal of the Korean Biblia Society for Library and Information Science 36.2 pp.183-209 (2025) : 183.
Heejun Kim, Bogeum Choi, Jaime Arguello. Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge. 2025; 36(2), 183-209.
Heejun Kim, Bogeum Choi and Jaime Arguello. "Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge" Journal of the Korean Biblia Society for Library and Information Science 36, no.2 (2025) : 183-209.
Heejun Kim; Bogeum Choi; Jaime Arguello. Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge. Journal of the Korean Biblia Society for Library and Information Science, 36(2), 183-209.
Heejun Kim; Bogeum Choi; Jaime Arguello. Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge. Journal of the Korean Biblia Society for Library and Information Science. 2025; 36(2) 183-209.
Heejun Kim, Bogeum Choi, Jaime Arguello. Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge. 2025; 36(2), 183-209.
Heejun Kim, Bogeum Choi and Jaime Arguello. "Understanding Health Information Credibility across UGC Platforms: Varying Influences of Credibility Features and Prior Knowledge" Journal of the Korean Biblia Society for Library and Information Science 36, no.2 (2025) : 183-209.
