HCC: Small: Helping People Negoiate Uncertain Information Online

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

This proposal explores how individuals decide what online information to trust given the uncertain information they often encounter. In today's society, life experiences are often processed in an online world. Online resources provide information and support for parenting, health, hobbies, news, and more. However, online resources are often incomplete, may include a diversity of opinions, and may be inaccurate. In a connected series of research studies exploring general theoretical questions, this project focuses on a compelling and common example of uncertainty online - the uncertainty about treatments for a chronic health condition. Chronic disease is a leading cause of ill health world wide, and one in ten Americans lives with a life-altering chronic condition. Unlike acute conditions (such as a high fever), chronic conditions, such as HIV, diabetes, arthritis, and Lyme disease, are prolonged and rarely cured completely. For this reason, management of chronic conditions lies much more in the hands of the patient. This research will study online health resources and individuals' use of these online resources using interview data, survey data, and text analysis of thousands of pages and posts available in online content. First, it will characterize the uncertainty of different types of online resources. Second, it will focus on how uncertainty impacts a specific community characterized by highly uncertain and even controversial information about the disease process and treatment (the Lyme disease community). And third, the research will test the generalizability of results in a complementary setting (such as individuals with chronic arthritis). The empirical work will answer the following questions: 1) How does the existence of incomplete, divergent and/or conflicting information affect the health choices made by individuals with chronic illness? 2) What factors (community, time, exposure to information) are critical to an individual with chronic illness deciding whether to believe in a specific viewpoint? The results will drive the design of two technological interventions that can improve people's ability to understand and decide among online resources: (1) A tool to extract and highlight key parameters of decision making derived from the research, that will crawl relevant sources and extract information such as patient consensus, medical research timeline, and risks. (2) A tool to classify online resources in terms of viewpoint, leveraging machine-learning techniques such as co-training to learn classifications on the fly. The second tool will inform the first, but also provide an interface for sorting and filtering online information and compare the information cloud associated with different viewpoints. The results of this research will add to existing knowledge about how the Internet can support individuals with chronic conditions, and contribute to the development of curriculum for courses on human-computer interaction in the medical area.