Assessing Target Audiences of Digital Public Health Campaigns: A Computational Approach

机译：评估数字公共卫生运动的目标受众：计算方法

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As a larger proportion of society participates in social media, public health organizations are increasingly using digital campaigns to engage and educate their target audiences. Computational methods such as social network analysis and machine learning can provide social media campaigns with a rare opportunity to better understand their followers at scale. In this short paper, we demonstrate how such methods can help inform program evaluation through a case study of FDA's The Real Cost anti-smoking Twitter campaign (@know-therealcost). By mining publicly available Twitter data, campaigns can identify and understand key communities to help maximize reach of campaign messages to their target audiences.

机译：作为一个更大比例的社会参与社交媒体，公共卫生组织越来越多地使用数字运动来参与和教育目标受众。社交网络分析和机器学习等计算方法可以提供社交媒体竞选，难得的机会更好地以规模更好地了解他们的追随者。在这篇短文中，我们展示了这些方法如何通过FDA的实际成本反抽吸Twitter运动（@诀窍）的案例研究来通过案例研究来提供人们。通过挖掘公开的推特数据，活动可以识别和理解关键社区，以帮助最大限度地将竞选消息范围最大限度地到目标受众。

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《International Conference on Social, Cultural, and Behavioral Modeling Prediction and Behavior Representation in Modeling and Simulation》|2018年|410p|共6页
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Robert F. Chew; Annice Kim; Vivian Chen; Paul Ruddle; Antonio Morgan-Lopez;
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中图分类 TP15-53;
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Social network analysis; Machine learning; Social media; Public health; Tobacco control;

机译：社交网络分析;机器学习;社交媒体;公共卫生;烟草控制;

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Assessing Target Audiences of Digital Public Health Campaigns: A Computational Approach

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