Baumgartner Peter, Peiper Nicholas
Center for Data Science, RTI International, Durham, NC, USA.
Behavioral Health and Criminal Justice Research Division, RTI International, Durham, NC, USA.
Subst Abuse. 2017 Jun 6;11:1178221817711425. doi: 10.1177/1178221817711425. eCollection 2017.
Large shifts in medical, recreational, and illicit cannabis consumption in the United States have implications for personalizing treatment and prevention programs to a wide variety of populations. As such, considerable research has investigated clinical presentations of cannabis users in clinical and population-based samples. Studies leveraging big data, social media, and social network analysis have emerged as a promising mechanism to generate timely insights that can inform treatment and prevention research. This study extends a novel method called stochastic block modeling to derive communities of cannabis consumers as part of a complex social network on Twitter. A set of examples illustrate how this method can ascertain candidate samples of medical, recreational, and illicit cannabis users. Implications for research planning, intervention design, and public health surveillance are discussed.
美国医疗、娱乐和非法大麻消费的巨大变化对针对广泛人群的个性化治疗和预防计划具有影响。因此，大量研究调查了临床和基于人群样本中大麻使用者的临床表现。利用大数据、社交媒体和社交网络分析的研究已成为一种有前景的机制，可产生能为治疗和预防研究提供信息的及时见解。本研究扩展了一种名为随机块建模的新方法，以在推特上作为复杂社交网络的一部分推导大麻消费者群体。一组示例说明了该方法如何确定医疗、娱乐和非法大麻使用者的候选样本。还讨论了对研究规划、干预设计和公共卫生监测的影响。
