Correia Rion Brattig, Wood Ian B, Bollen Johan, Rocha Luis M
Instituto Gulbenkian de Cincia, 2780-156 Oeiras, Portugal.
Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, Indiana 47408, USA.
Annu Rev Biomed Data Sci. 2020 Jul;3:433-458. doi: 10.1146/annurev-biodatasci-030320-040844. Epub 2020 May 4.
Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.
社交媒体数据越来越多地被用于研究生物医学和健康相关现象。从对某种疾病的队列层面讨论到对情绪的人群层面分析，社交媒体为科学家提供了前所未有的大量数据，用于研究与各种健康状况和医学治疗相关的人类行为。在此，我们回顾了近期在挖掘社交媒体以获取与人类健康的多层次复杂性相关的生物医学、流行病学和社会现象信息方面的工作。我们特别关注社交媒体数据分析取得最大进展的主题，包括药物警戒和情绪分析，尤其是针对心理健康方面的。我们还讨论了社交媒体数据在健康相关应用中的各种创新用途以及社交媒体数据获取和使用的重要局限性。
