Many health behaviors and decisions, despite being an individual choice, are often influenced by social and cultural context. As people's activities and communication on the Web increase, information about their social activities and interactions become more available. User-generated contents on the social web including Twitter, Facebook, Instagram, web sites, and blogs, as well as the future semantic web, afford us enormous opportunity for health-related research. Today’s researchers are mining these big datasets for patterns and trends that can lead to new hypotheses, new discoveries, and new interventions. How can you tap into the wealth of social web data to understand the intersections of individual behaviors, social-environmental factors, and social interactions to answer scientific questions? How can you leverage the new communication landscape (Internet penetration, online communities, social media, and wikis) to design health interventions and improve measurement of outcomes (e.g., clinical trial recruitment, risk perception, decision making, and attitudes)? Nevertheless, there are also numerous challenges in using social web data for health-related research. For example, social web users are different from the general population. Twitter users are younger than the general population. Thus, the representativeness of social media populations should be carefully considered when interpreting study findings. The presence of bots and fake accounts may also distort the representativeness of our findings. Further, the way how social media data are collected may lead to variations in the sampling units in social media studies, which may lead to data selection bias. An adequate understanding of the inherent limitations in social media data is always important. Many social media and health related studies leverage state-of-the-art machine-/deep-learning methods and tools. For example, sentiment analysis and topic modeling approaches are widely used in social media studies to tease out public’s perception and attitudes towards specific health topics. Nerveless, novel intelligent systems are needed to push the field forward.
We are inviting original research submissions (FULL 12 pages) and work-in-progress (SHORT 6 pages). Submitted papers must be formatted using the Springer LNCS/LNAI style.
Topics of interest include but not limited to:
Note that, all papers have to contain 3 essential themes: (1) use of social media data, (2) use of artificial intelligence methods, and (3) focusing on health-related problems.
Submission Site: https://easychair.org/conferences/?conf=isswhr2020
Jiang Bian, PhD, Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
Mattia Prosperi, PhD, Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, FL, USA
Kenji Yoshigoe, PhD, Faculty of Information Networking for Innovation and Design, Toyo University, Tokyo, Japan
Yi Guo, PhD, Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
Cui Tao, PhD, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, Texas, USA