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Big social data analytics for public health: Facebook engagement and performance

机译:公共卫生的大社会数据分析:Facebook参与和表现

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In recent years, social media has offered new opportunities for interaction and distribution of public health information within and across organisations. In this paper, we analysed data from Facebook walls of 153 public organisations using unsupervised machine learning techniques to understand the characteristics of user engagement and post performance. Our analysis indicates an increasing trend of user engagement on public health posts during recent years. Based on the clustering results, our analysis shows that Photo and Link type posts are most favourable for high and medium user engagement respectively.
机译:近年来,社会媒体为在组织内部和组织内部和跨国组织的互动和分销提供了新的机会。在本文中,我们使用无监督的机器学习技术分析了来自153个公共组织的Facebook墙壁的数据,以了解用户参与和后期性能的特征。我们的分析表明,近年来,在公共卫生职位上的用户参与越来越大。根据聚类结果,我们的分析表明,照片和链接类型帖子分别对高中和中等用户接合最有利。

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