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首页> 外文期刊>Journal of health communication >Combining Crowd-Sourcing and Automated Content Methods to Improve Estimates of Overall Media Coverage: Theme Mentions in E-cigarette and Other Tobacco Coverage
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Combining Crowd-Sourcing and Automated Content Methods to Improve Estimates of Overall Media Coverage: Theme Mentions in E-cigarette and Other Tobacco Coverage

机译：结合人群采购和自动化内容方法，以改善整体媒体覆盖率的估计：电子烟和其他烟草覆盖中的主题提及

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Exposure to media content can shape public opinions about tobacco. Accurately describing content is a first step to showing such effects. Historically, content analyses have hand-coded tobacco-focused texts from a few media sources which ignored passing mention coverage and social media sources, and could not reliably capture over-time variation. By using a combination of crowd-sourced and automated coding, we labeled the population of all e-cigarette and other tobacco-related (including cigarettes, hookah, cigars, etc.) 'long-form texts' (focused and passing coverage, in mass media and website articles) and social media items (tweets and YouTube videos) collected May 2014-June 2017 for four tobacco control themes. Automated coding of theme coverage met thresholds for item-level precision and recall, event validation, and weekly-level reliability for most sources, except YouTube. Health, Policy, Addiction and Youth themes were frequent in e-cigarette long-form focused coverage (44%-68%), but not in long-form passing coverage (5%-22%). These themes were less frequent in other tobacco coverage (long-form focused (13-32%) and passing coverage (4-11%)). Themes were infrequent in both e-cigarette (1-3%) and other tobacco tweets (2-4%). Findings demonstrate that passing e-cigarette and other tobacco long-form coverage and social media sources paint different pictures of theme coverage than focused long-form coverage. Automated coding also allowed us to code the amount of data required to estimate reliable weekly theme coverage over three years. E-cigarette theme coverage showed much more week-to-week variation than did other tobacco coverage. Automated coding allows accurate descriptions of theme coverage in passing mentions, social media, and trends in weekly theme coverage.
机译：接触媒体内容可以塑造关于烟草的公众意见。准确描述内容是显示这种效果的第一步。从历史上看，内容分析具有来自几个媒体来源的手工编码的烟草专注的文本，忽略了通过提及覆盖范围和社交媒体来源，并且无法可靠地捕获过度变化。通过使用人群资源和自动编码的组合，我们标记了所有电子烟和其他烟草相关（包括香烟，水烟，雪茄等）的人口“长形文本”（集中并通过覆盖，大众媒体和网站文章）和社交媒体项目（推文和YouTube视频）于2014年5月 - 2017年6月为四个烟草控制主题。主题覆盖的自动编码适用于项目级精度和召回，事件验证以及大多数源以外的阈值，除了YouTube之外的大多数源。健康，政策，成瘾和青少年主题在电子烟长形覆盖率（44％-68％）中经常出现（44％-68％），但不得长期通过覆盖率（5％-22％）。这些主题在其他烟草覆盖范围内越频繁（长形聚焦（13-32％）和通过覆盖率（4-11％））。电子烟（1-3％）和其他烟草推文（2-4％）中，主题罕见。调查结果表明，通过电子烟和其他烟草长形覆盖和社交媒体来源绘制的主题覆盖范围的不同图片，而不是集中的长形覆盖范围。自动编码也允许我们编写三年内估计可靠的每周主题覆盖所需的数据量。电子烟主题覆盖范围显示比其他烟草覆盖率更多的一周到一周的变化。自动编码允许准确描述通过提及，社交媒体和每周主题覆盖范围的主题覆盖范围。

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