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

社交媒体为公共卫生研究创造了新的机会，与传统的调查方法相比，它以更低的成本提供了更大的覆盖面。与传统调查数据相比，在线内容提供了几个潜在优势；人们可以实时测量行为和态度如何随着法律变化、新产品和营销活动等罕见事件而变化。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，可能会使结果产生偏差。本提案开发了克服这一问题的技术，使公共卫生研究能够有效利用可公开获得的社会媒体数据。根据传统的基于调查的方法对这些方法进行评估，以评估现实世界公共卫生情景中的端到端有效性，确定戒烟运动的有效性。该项目建立在基础良好的统计方法上，以消除分类器偏差。关键的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，对混淆变量的鲁棒性以及识别比较组的可扩展方法。有噪声的数据将通过先进的多重插值技术来处理。该项目将开发一种基于模型的方法来识别解决混淆变量问题的比较组。这些方法将在一项关于戒烟的实际公共卫生研究的背景下进行评估，该研究基于历史Twitter数据、疾病预防控制中心活动前后进行的传统调查，以及对吸烟者进行的关于电子烟感知风险因素的调查。

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.