IEEE/ACM Trans Comput Biol Bioinform. 2022 Jan-Feb;19(1):180-191. doi: 10.1109/TCBB.2020.2978476. Epub 2022 Feb 3.
The opioid abuse epidemic represents a major public health threat to global populations. The role social media may play in facilitating illicit drug trade is largely unknown due to limited research. However, it is known that social media use among adults in the US is widespread, there is vast capability for online promotion of illegal drugs with delayed or limited deterrence of such messaging, and further, general commercial sale applications provide safeguards for transactions; however, they do not discriminate between legal and illegal sale transactions. These characteristics of the social media environment present challenges to surveillance which is needed for advancing knowledge of online drug markets and the role they play in the drug abuse and overdose deaths. In this paper, we present a computational framework developed to automatically detect illicit drug ads and communities of vendors. The SVM- and CNN- based methods for detecting illicit drug ads, and a matrix factorization based method for discovering overlapping communities have been extensively validated on the large dataset collected from Google+, Flickr and Tumblr. Pilot test results demonstrate that our computational methods can effectively identify illicit drug ads and detect vendor-community with accuracy. These methods hold promise to advance scientific knowledge surrounding the role social media may play in perpetuating the drug abuse epidemic.
阿片类药物滥用危机是全球范围内的主要公共卫生威胁。社交媒体在促进非法毒品交易方面可能发挥的作用由于研究有限而在很大程度上尚未可知。然而，已知的是，美国成年人中社交媒体的使用非常普遍，大量在线推广非法毒品的能力具有滞后性或有限的威慑力，而且一般的商业销售应用程序为交易提供了保障；但是，它们不能区分合法和非法销售交易。社交媒体环境的这些特征给监测带来了挑战，监测对于了解在线毒品市场及其在药物滥用和过量死亡中的作用是必要的。在本文中，我们提出了一个计算框架，用于自动检测非法毒品广告和供应商社区。基于支持向量机和卷积神经网络的方法用于检测非法毒品广告，以及基于矩阵分解的方法用于发现重叠社区，已经在从 Google+、Flickr 和 Tumblr 收集的大型数据集上进行了广泛验证。试点测试结果表明，我们的计算方法可以有效地识别非法毒品广告并以高精度检测供应商社区。这些方法有望推进围绕社交媒体在延续药物滥用危机方面可能发挥的作用的科学知识。
