Mackey Tim K, Kalyanam Janani, Katsuki Takeo, Lanckriet Gert
Tim K. Mackey is with the Department of Anesthesiology and Department of Medicine, University of California, San Diego, and the Global Health Policy Institute, San Diego. Janani Kalyanam is with the Global Health Policy Institute and the Department of Electrical and Computer Engineering, University of California, San Diego. Takeo Katsuki is with the Kavli Institute for Brain and Mind, University of California, San Diego. Gert Lanckriet is with the Department of Electrical and Computer Engineering, University of California, San Diego.
Am J Public Health. 2017 Dec;107(12):1910-1915. doi: 10.2105/AJPH.2017.303994. Epub 2017 Oct 19.
To deploy a methodology accurately identifying tweets marketing the illegal online sale of controlled substances.
We first collected tweets from the Twitter public application program interface stream filtered for prescription opioid keywords. We then used unsupervised machine learning (specifically, topic modeling) to identify topics associated with illegal online marketing and sales. Finally, we conducted Web forensic analyses to characterize different types of online vendors. We analyzed 619 937 tweets containing the keywords codeine, Percocet, fentanyl, Vicodin, Oxycontin, oxycodone, and hydrocodone over a 5-month period from June to November 2015.
A total of 1778 tweets (< 1%) were identified as marketing the sale of controlled substances online; 90% had imbedded hyperlinks, but only 46 were "live" at the time of the evaluation. Seven distinct URLs linked to Web sites marketing or illegally selling controlled substances online.
Our methodology can identify illegal online sale of prescription opioids from large volumes of tweets. Our results indicate that controlled substances are trafficked online via different strategies and vendors. Public Health Implications. Our methodology can be used to identify illegal online sellers in criminal violation of the Ryan Haight Online Pharmacy Consumer Protection Act.
采用一种方法准确识别推销受控物质非法网上销售的推文。
我们首先从推特公共应用程序接口流中收集经过处方阿片类药物关键词过滤的推文。然后我们使用无监督机器学习（具体而言，主题建模）来识别与非法网络营销和销售相关的主题。最后，我们进行网络取证分析以描述不同类型的在线供应商。我们在2015年6月至11月的5个月期间分析了619937条包含可待因、羟考酮、芬太尼、维柯丁、奥施康定、羟考酮和氢可酮等关键词的推文。
总共1778条推文（<1%）被确定为推销受控物质的网上销售；90%包含嵌入超链接，但在评估时只有46个是“活跃的”。七个不同的网址链接到推销或非法在线销售受控物质 的网站。
我们的方法可以从大量推文中识别出处方阿片类药物的非法网上销售。我们的结果表明，受控物质通过不同的策略和供应商在网上进行贩运。对公共卫生的影响。我们的方法可用于识别违反《瑞安·海特在线药房消费者保护法》的非法网上卖家。
