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，比传统的调查方法成本更低，覆盖面更广。 与传统的调查数据相比，在线内容提供了几个潜在的优势;人们可以实时测量行为和态度如何随着法律的变化、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改善健康结果的干预措施，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。 该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公开的社交媒体数据。 这些方法与传统的基于调查的方法进行了评估，以评估真实世界公共卫生场景中的端到端有效性，确定戒烟活动的有效性。该项目建立在基础良好的统计方法基础上，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）的高维噪声域，对混淆变量的鲁棒性，以及识别比较组的可扩展方法。 噪声数据将通过推进多重插补技术来解决。 该项目将开发一种基于模型的方法，以确定解决混淆变量问题的比较组。 这些方法将在实际的戒烟公共卫生研究的背景下进行评估，基于历史Twitter数据和CDC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.