Center for Infectious Disease Dynamics, Department of Biology, Penn State University, University Park, Pennsylvania, United States of America.
PLoS Comput Biol. 2011 Oct;7(10):e1002199. doi: 10.1371/journal.pcbi.1002199. Epub 2011 Oct 13.
There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness.
人们对社交网络中健康行为的动态及其对集体公共卫生结果的影响非常感兴趣，但要在时间和空间上衡量人口健康行为，需要大量资源。在这里，我们使用了从在线社交媒体上的 101853 名用户中收集的公开数据，这些数据是在将近六个月的时间内收集的，用于衡量人们对一种新疫苗的时空情绪。我们通过识别在线表达的情绪与疾病预防控制中心按地区估计的疫苗接种率之间的强相关性，验证了我们的方法。对有意见的用户网络的分析表明，信息在具有相同情绪的用户之间流动的频率高于随机情况下的预期，而在没有相同情绪的用户之间流动的频率则低于预期。我们还发现，大多数社区对新型疫苗的情绪要么是积极的，要么是消极的。传染病传播的模拟表明，如果负面疫苗情绪的集群导致未受保护的个体集群，那么疾病爆发的可能性将大大增加。在线社交媒体提供了前所未有的数据访问权限，从而可以使用廉价且高效的工具来识别干预措施的目标区域，并评估其效果。
