Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15.
Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15.
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DOI：
10.1016/j.socscimed.2017.08.041
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发表时间：
2017-10
期刊：
影响因子：
--
通讯作者：
El-Toukhy S
中科院分区：
文献类型：
--
作者：
Tomeny TS;Vargo CJ;El-Toukhy S
这项研究考察了2009-2015年Twitter上反疫苗信念的时间趋势，地理分布和人口统计学相关性。共有549，972条推文被下载并通过机器学习算法编码为反疫苗信念。带有自我披露的地理信息的推文得到了解决，并在小城市/大城市一级收集了相应地区的美国人口普查数据。随着时间的推移，在国家和州一级检查了反疫苗推文数量的趋势。使用最小绝对收缩和选择算子回归模型来确定与反疫苗接种推特数量相关的人口普查变量。我们在2009年至2015年收集的549，972条推文样本中，有50%包含反疫苗信念。在疫苗相关新闻报道后，反疫苗推文量增加。加州、康涅狄格州、马萨诸塞州、纽约和宾夕法尼亚州的反疫苗推文数量偏离了全国平均水平。人口统计学特征解释了反疫苗推文地理聚集的67%的方差，这与最近生育的女性，高收入家庭，40至44岁的男性和大学教育程度最低的男性的人口数量和浓度较高有关。在Twitter上监测反疫苗接种的信念可以发现与疫苗相关的担忧和误解，作为公众舆论转变的指标，并使儿科医生能够反驳反疫苗的论点。需要实时干预措施来对抗网上的反疫苗接种观念。识别反疫苗接种信念的集群可以帮助公共卫生专业人员向地理位置和人口统计部门传播有针对性的/量身定制的干预措施。
This study examines temporal trends, geographic distribution, and demographic correlates of anti-vaccine beliefs on Twitter, 2009–2015. A total of 549,972 tweets were downloaded and coded for the presence of anti-vaccine beliefs through a machine learning algorithm. Tweets with self-disclosed geographic information were resolved and United States Census data were collected for corresponding areas at the micropolitan/metropolitan level. Trends in number of anti-vaccine tweets were examined at the national and state levels over time. A least absolute shrinkage and selection operator regression model was used to determine census variables that were correlated with anti-vaccination tweet volume. Fifty percent of our sample of 549,972 tweets collected between 2009 and 2015 contained anti-vaccine beliefs. Anti-vaccine tweet volume increased after vaccine-related news coverage. California, Connecticut, Massachusetts, New York, and Pennsylvania had anti-vaccination tweet volume that deviated from the national average. Demographic characteristics explained 67% of variance in geographic clustering of anti-vaccine tweets, which were associated with a larger population and higher concentrations of women who recently gave birth, households with high income levels, men aged 40 to 44, and men with minimal college education. Monitoring anti-vaccination beliefs on Twitter can uncover vaccine-related concerns and misconceptions, serve as an indicator of shifts in public opinion, and equip pediatricians to refute anti-vaccine arguments. Real-time interventions are needed to counter anti-vaccination beliefs online. Identifying clusters of anti-vaccination beliefs can help public health professionals disseminate targeted/tailored interventions to geographic locations and demographic sectors of the population.
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作者：
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