HCC: Small: Helping People Negoiate Uncertain Information Online

HCC：小：帮助人们在线协商不确定的信息

• 批准号: 0916459
• 负责人: Jennifer Mankoff
• 金额: $ 49.21万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916459&HistoricalAwards=false
• 关键词: HCC; Small; Helping; People; Negoiate

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.

这项建议探讨了在经常遇到不确定信息的情况下，个人如何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。在线资源提供有关育儿、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，可能包含不同的观点，并且可能不准确。在探索一般理论问题的一系列相关研究中，该项目重点关注在线不确定性的一个引人注目的常见例子--慢性健康状况治疗的不确定性。慢性病是世界范围内健康不佳的主要原因，十分之一的美国人生活在改变生活的慢性病中。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，很少能完全治愈。因此，慢性病的管理更多地掌握在患者手中。本研究将利用访谈数据、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，它将表征不同类型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，该社区的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，这项研究将测试结果在互补环境(如慢性关节炎患者)中的普适性。实证工作将回答以下问题：1)不完整、不同和/或相互冲突的信息的存在如何影响慢性病患者做出的健康选择？2)慢性病患者决定是否相信特定观点的关键因素(社区、时间、信息暴露)是什么？其结果将推动两种技术干预措施的设计，这些干预措施可以提高人们在在线资源中理解和决策的能力：(1)提取和突出研究得出的决策关键参数的工具，它将抓取相关来源，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，利用机器学习技术，如联合培训，动态学习分类。第二个工具将通知第一个，但也提供了一个界面，用于对在线信息进行分类和过滤，并比较与不同观点相关联的信息云。这项研究的结果将增加关于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开发。