Despite the overwhelming quantity of health information that is available online today, finding reliable and trustworthy information is still challenging, even when advanced recommender systems are used. To tackle this challenge and improve our recommended sources, we need to first understand and capture the user behavior of what is considered to be trustworthy. This paper presents a taxonomy of relevant factors that influence user' trust towards online health information sources. We design a survey experiment to validate the taxonomical factors, and propose an ontology using the taxonomy of factors, such that each user's trust could be modeled as an instance of our ontology and this could later be used programmatically to recommend trustworthy information sources. Our work will inform the design of personalized recommendation systems and websites to improve online delivery of health information.
Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques
Oak Ridge National Laboratory（橡树岭国家实验室）；University of Tennessee Health Science Center（田纳西大学健康科学中心）
National Institute of Advanced Industrial Science and Technology (AIST)（独立行政法人产业技术综合研究所）；Research Organization of Information and Systems（信息·系统研究机构）；The University of Tokyo（东京大学）；Kitami Institute of Technology（北见工业大学）
A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbots
University of Illinois Urbana-Champaign, Urbana, United States（伊利诺伊大学厄巴纳-香槟分校）；Indiana University Indianapolis, Indianapolis, United States（印第安纳大学印第安纳波利斯分校）
