HCC: Small: Helping People Negoiate Uncertain Information Online

该提案探讨了个人如何在他们经常遇到的不确定信息的情况下决定信任哪些在线信息。在当今社会，生活经历往往是在网络世界中处理的。在线资源提供育儿、健康、爱好、新闻等方面的信息和支持。然而，在线资源往往是不完整的，可能包括各种各样的观点，可能是不准确的。在一系列探索一般理论问题的相关研究中，该项目侧重于在线不确定性的一个引人注目和常见的例子-慢性健康状况治疗的不确定性。慢性病是世界范围内导致健康不佳的主要原因，十分之一的美国人患有改变生活的慢性病。与急性疾病（如高烧）不同，慢性疾病，如艾滋病毒、糖尿病、关节炎和莱姆病，会持续很长时间，而且很少能完全治愈。由于这个原因，慢性疾病的管理更多地掌握在患者手中。本研究将通过访谈数据、调查数据和对在线内容中数千页和帖子的文本分析，研究在线健康资源和个人对这些在线资源的使用情况。首先，它将描述不同类型在线资源的不确定性。其次，它将关注不确定性如何影响一个特定的社区，其特征是关于疾病过程和治疗的高度不确定甚至有争议的信息（莱姆病社区）。第三，这项研究将在一个互补的环境中测试结果的普遍性（比如患有慢性关节炎的个体）。实证工作将回答以下问题：1)不完整、分歧和/或冲突信息的存在如何影响慢性病患者的健康选择？2)哪些因素（社区、时间、接触信息）对慢性病患者决定是否相信某一特定观点至关重要？研究结果将推动两种技术干预措施的设计，以提高人们对在线资源的理解和决策能力：(1)提取和突出显示从研究中得出的决策关键参数的工具，该工具将抓取相关来源并提取患者共识、医学研究时间表和风险等信息。(2)根据观点对在线资源进行分类的工具，利用协同训练等机器学习技术在飞行中学习分类。第二个工具将通知第一个工具，但也提供了一个接口，用于排序和过滤在线信息，并比较与不同观点相关的信息云。这项研究的结果将增加有关互联网如何支持慢性病患者的现有知识，并有助于医学领域人机交互课程的课程开发。

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.