HCC: Small: Helping People Negoiate Uncertain Information Online

该提案探讨了个人如何在经常遇到不确定信息的情况下决定信任哪些在线信息。 在当今社会，生活经验往往是在网络世界中处理。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，可能包括不同的意见，并可能是不准确的。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令人信服和常见的例子-慢性健康状况治疗的不确定性。慢性病是世界范围内健康状况不佳的主要原因，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，是长期的，很少完全治愈。因此，慢性病的管理更多地掌握在患者手中。本研究将通过访谈数据、调查数据以及对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，它将描述不同类型的在线资源的不确定性。其次，它将侧重于不确定性如何影响一个特定的社区，其特点是关于疾病过程和治疗的信息高度不确定，甚至有争议（莱姆病社区）。第三，这项研究将测试结果在互补环境（如慢性关节炎患者）中的普遍性。实证工作将回答以下问题：1）不完整，分歧和/或冲突的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、信息接触）对慢性病患者决定是否相信某个特定观点至关重要？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）一种工具，用于提取和突出研究中的关键决策参数，该工具将抓取相关来源并提取信息，如患者共识，医学研究时间轴和风险。(2)一种根据观点对在线资源进行分类的工具，利用机器学习技术（如联合训练）动态学习分类。第二个工具将为第一个工具提供信息，但也提供一个界面，用于对在线信息进行分类和过滤，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，并有助于开发医学领域人机交互课程的课程。

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.