Fung Isaac Chun-Hai, Duke Carmen Hope, Finch Kathryn Cameron, Snook Kassandra Renee, Tseng Pei-Ling, Hernandez Ana Cristina, Gambhir Manoj, Fu King-Wa, Tse Zion Tsz Ho
Department of Epidemiology, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA.
Department of Epidemiology, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA.
Am J Infect Control. 2016 Dec 1;44(12):1660-1671. doi: 10.1016/j.ajic.2016.05.011. Epub 2016 Jul 15.
We systematically reviewed existing research pertinent to Ebola virus disease and social media, especially to identify the research questions and the methods used to collect and analyze social media.
We searched 6 databases for research articles pertinent to Ebola virus disease and social media. We extracted the data using a standardized form. We evaluated the quality of the included articles.
Twelve articles were included in the main analysis: 7 from Twitter with 1 also including Weibo, 1 from Facebook, 3 from YouTube, and 1 from Instagram and Flickr. All the studies were cross-sectional. Eleven of the 12 articles studied ≥ 1of these 3 elements of social media and their relationships: themes or topics of social media contents, meta-data of social media posts (such as frequency of original posts and reposts, and impressions) and characteristics of the social media accounts that made these posts (such as whether they are individuals or institutions). One article studied how news videos influenced Twitter traffic. Twitter content analysis methods included text mining (n = 3) and manual coding (n = 1). Two studies involved mathematical modeling. All 3 YouTube studies and the Instagram/Flickr study used manual coding of videos and images, respectively.
Published Ebola virus disease-related social media research focused on Twitter and YouTube. The utility of social media research to public health practitioners is warranted.
我们系统回顾了与埃博拉病毒病和社交媒体相关的现有研究，尤其旨在确定研究问题以及用于收集和分析社交媒体的方法。
我们在6个数据库中搜索与埃博拉病毒病和社交媒体相关的研究文章。我们使用标准化表格提取数据。我们评估了纳入文章的质量。
12篇文章纳入主要分析：7篇来自推特，其中1篇还包括微博，1篇来自脸书，3篇来自优兔，1篇来自照片墙和Flickr。所有研究均为横断面研究。12篇文章中的11篇研究了社交媒体的这3个要素中的至少1个及其关系：社交媒体内容的主题或话题、社交媒体帖子的元数据（如原创帖子和转发的频率以及曝光量）以及发布这些帖子的社交媒体账号的特征（如它们是个人还是机构）。1篇文章研究了新闻视频如何影响推特流量。推特内容分析方法包括文本挖掘（n = 3）和人工编码（n = 1）。两项研究涉及数学建模。所有3篇优兔研究以及照片墙/Flickr研究分别对视频和图像进行了人工编码。
已发表的与埃博拉病毒病相关的社交媒体研究主要集中在推特和优兔上。社交媒体研究对公共卫生从业者的实用性是有依据的。
