III: Small: Collaborative Research: Reducing Classifier Bias in Social Media Studies of Public Health

III：小：合作研究：减少公共卫生社交媒体研究中的分类器偏差

• 批准号: 1659139
• 负责人: Sherry Emery
• 金额: $ 14万
• 依托单位: National Opinion Research Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659139&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Reducing

Social media creates a new opportunity for public health research, giving greater reach at lower cost than traditional survey methods. Online content offers several potential advantages over traditional survey data; one can in real-time measure how behaviors and attitudes change in response to rare events such as legal changes, new products, and marketing campaigns. Machine learning techniques for classification can be used to tailor interventions that improve health outcomes while minimizing costs. However, online content is not a random sample, potentially biasing the outcomes. This proposal develops techniques to overcome this problem, enabling effective use of publicly available social media data for public health research. The approaches are evaluated against a traditional survey-based approach to evaluate end-to-end effectiveness in a real-world public health scenario, determining effectiveness of smoking cessation campaigns.The project builds on well-grounded statistical approaches to eliminate classifier bias. Key innovations are extending this to the high-dimensional, noisy domain of textual social media data (specifically Twitter), robustness to confounding variables, and scalable methods to identify comparison groups. Noisy data will be addressed through advancing multiple imputation techniques. The project will develop a model-based approach to identifying comparison groups that addresses confounding variable issues. The methods will be evaluated in the context of an actual public health study of smoking cessation, based on historical Twitter data and traditional surveys conducted before and after a CDC campaign as well as a survey of smokers on perceived risk factors of e-cigarettes.

社交媒体为公共卫生研究创造了新的机会，比传统调查方法以更低的成本提供更大的影响范围。 与传统调查数据相比，在线内容具有多种潜在优势；人们可以实时衡量行为和态度如何因法律变更、新产品和营销活动等罕见事件而发生变化。 用于分类的机器学习技术可用于定制干预措施，以改善健康结果，同时最大限度地降低成本。 然而，在线内容不是随机样本，可能会导致结果出现偏差。 该提案开发了克服这一问题的技术，从而能够有效利用公开的社交媒体数据进行公共卫生研究。 这些方法根据传统的基于调查的方法进行评估，以评估现实世界公共卫生场景中的端到端有效性，从而确定戒烟运动的有效性。该项目建立在有根据的统计方法的基础上，以消除分类器偏差。 关键创新是将其扩展到文本社交媒体数据（特别是 Twitter）的高维、嘈杂领域、对混杂变量的鲁棒性以及识别对照组的可扩展方法。 噪声数据将通过先进的多重插补技术来解决。 该项目将开发一种基于模型的方法来识别比较组，以解决混杂的变量问题。 这些方法将在戒烟的实际公共卫生研究的背景下进行评估，基于历史推特数据和疾病预防控制中心活动前后进行的传统调查，以及对吸烟者感知电子烟风险因素的调查。