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Drug Saf. 2016 Mar;39(3):231-40. doi: 10.1007/s40264-015-0379-4.

Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter.

Author information

Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
Rueckert-Hartman College for Health Professions, Regis University, Denver, CO, USA.
Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, USA.



Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications.


Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts.


We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall(®), oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time.


Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall(®): 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time.


Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.

[Indexed for MEDLINE]
Free PMC Article

Conflict of interest statement

Compliance with Ethical Standards Funding This work was supported by National Institutes of Health (NIH) National Library of Medicine (NLM) grant number NIH NLM 5R01LM011176. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NLM or NIH. Conflict of interest Abeed Sarker, Rachel Ginn, Karen O’Connor, Matthew Scotch, Karen Smith, Dan Malone, and Graciela Gonzalez have no conflicts of interest that are directly relevant to the content of this study. Ethical approval Not applicable. Informed consent Not applicable.

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