Media content can shape people's descriptive norm perceptions by presenting either population-level prevalence information or descriptions of individuals' behaviors. Supervised machine learning and crowdsourcing can be combined to answer new, theoretical questions about the ways in which normative perceptions form and evolve through repeated, incidental exposure to normative mentions emanating from the media environment. Applying these methods, this study describes tobacco and e-cigarette norm prevalence and trends over 37 months through an examination of a census of 135,764 long-form media texts, 12,262 popular YouTube videos, and 75,322,911 tweets. Long-form texts mentioned tobacco population norms (4-5%) proportionately less often than e-cigarette population norms (20%). Individual use norms were common across sources, particularly YouTube (tobacco long-form: 34%; Twitter: 33%; YouTube: 88%; e-cigarette long form: 17%; Twitter: 16%; YouTube: 96%). The capacity to capture aggregated prevalence and temporal dynamics of normative media content permits asking population-level media effects questions that would otherwise be infeasible to address.

中文翻译：

---

走向规范形成的聚合、隐式和动态模型：通过自动化和众包内容分析捕获动态描述规范的大规模媒体表示

媒体内容可以通过呈现人口水平的流行信息或个人行为的描述来塑造人们的描述性规范感知。有监督的机器学习和众包可以结合起来回答新的理论问题，这些问题是关于通过反复、偶然地接触媒体环境产生的规范性提及而形成和演变的规范性看法的方式。应用这些方法，本研究通过对 135,764 篇长格式媒体文本、12,262 个流行 YouTube 视频和 75,322,911 条推文的普查，描述了 37 个月内烟草和电子烟的规范流行率和趋势。长篇文本提到烟草人口规范（4-5%）的比例低于电子烟人口规范（20%）。各个来源的个人使用规范很常见，尤其是 YouTube（长篇烟草：34%；Twitter：33%；YouTube：88%；长篇电子烟：17%；Twitter：16%；YouTube：96%）。捕捉规范媒体内容的总体流行度和时间动态的能力允许提出人群层面的媒体影响问题，否则这些问题将无法解决。