Enabling Real-Time Drug Abuse Detection in Tweets
Enabling Real-Time Drug Abuse Detection in Tweets
复制标题
在推文中启用实时药物滥用检测
DOI：
10.1109/icde.2017.221
复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Geller
中科院分区：
文献类型：
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作者：
Nhathai Phan;Soon Ae Chun;Manasi Bhole;J. Geller
处方药滥用是美国增长最快的公共卫生问题之一。为了应对这一流行病，一种近乎实时的监测战略，而不是求助于追溯健康记录的战略，可能会改善对非法药物和处方药滥用的流行率和滥用模式的检测。在这篇文章中，我们的主要目标是展示利用社交媒体(如Twitter)自动监控非法药物和处方药滥用的可能性。我们使用机器学习方法进行自动分类，可以识别表明药物滥用的推文。我们收集了与知名非法和处方药相关的推文。我们手动注释了300条可能与药物滥用有关的推文。我们的实验比较了一组分类算法和决策树分类器J48，以及支持向量机在确定推文是否包含药物滥用信号方面的性能优于其他分类算法。这项自动监督分类研究的结果说明了Twitter在检查滥用模式方面的效用，并表明了构建能够近乎实时地处理来自社交媒体来源的海量数据的药物滥用检测系统的可行性。
Prescription drug abuse is one of the fastest growing public health problems in the USA. To address this epidemic, a near real-time monitoring strategy, instead of one resorting to a retrospective health records, may improve detecting the prevalence and patterns of abuse of both illegal drugs and prescription medications. In this paper, our primary goals are to demonstrate the possibility of utilizing social media, e.g., Twitter, for automatic monitoring of illegal drug and prescription medication abuse. We use machine learning methods for an automatic classification that can identify tweets that are indicative of drug abuse. We collected tweets associated with well-known illegal and prescription drugs. We manually annotated 300 tweets that are likely to be related to drug abuse. Our experiment compares a set of classification algorithms, and a decision tree classifier J48, and the SVM outperform others for determining whether tweets contain signals of drug abuse. This automatic supervised classification study results illustrate the utility of Twitter in examining patterns of abuse, and show the feasibility of building the drug abuse detection system that can process large volume data from social media sources in a near real-time.
影响因子：
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作者：
Moreno, Megan A.;Christakis, Dimitri A.;Egan, Katie G.;Brockman, Libby N.;Becker, Tara
通讯作者：
Becker, Tara
使用对数线性模型进行歧视发现和预防
DOI：
10.1109/dsaa.2016.18
发表时间：
2016
期刊：
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA
影响因子：
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作者：
Wu, Yongkai;Wu, Xintao
通讯作者：
Wu, Xintao