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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:一种计算方法

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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:随着越来越多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吸引和教育其目标受众。社交网络分析和机器学习等计算方法可以为社交媒体活动提供难得的机会,以更好地大规模了解其关注者。在这篇简短的论文中,我们将通过FDA的“真实成本”反吸烟Twitter活动(@ know-therealcost)的案例研究,说明这些方法如何帮助为计划评估提供信息。通过挖掘公开可用的Twitter数据,活动可以识别和了解关键社区,以帮助最大程度地将活动消息传递给目标受众。

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