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

Abstract

IntroductionPrescription 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.ObjectivesOur 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.MethodsWe 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.ResultsOur 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.ConclusionOur 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.

A Survey of Text Mining in Social Media: Facebook and Twitter Perspectives

“…Scholars of [58] developed and formulated an automatic classification technique through which potentially abuseindicating user posts could be identified and evaluating the likelihood of social media usage as a source for automatic monitoring of drug medication abuse. In this regard, Twitter user posts (tweets) were collected and these were linked with three commonly abused medications (Oxycodone, Adderall, and Quetiapine).…”

A Large-Scale CNN Ensemble for Medication Safety Analysis

“…To guarantee the diversity of the models, we consider CNNs that have different number of convolutional filters (200 -400), size of the convolutional filter (4)(5)(6)(7)(8) and dimension of word embeddings (200-300). The prediction of the committee is determined by a majority vote, and from the statistical viewpoint this combination of models is more powerful than a single one if sub-models are uncorrelated.…”

“…One competition was Diegolab-2015, where the goal was to develop an algorithm for solving the problems of ADR classification and extraction. The teams showed competitive results on a difficult Twitter dataset, and the best performance was achieved by [5] with 59% F1-score for ADR class.…”

A Survey of Current Work in Medical Text Mining---Data Source Perspective

“…Apart from disease surveillance and extraction of ADRs, it turns out that the web and social media (WSM) is also full of abuse-related information which could be used to monitor prescription medication abuse by Abeed Sarker, Karen O'Connor & Rachel Ginn etc. (2016) [6]. They take three commonly used abused medications (Adderall, Oxycodone and quetiapine) for testing by collecting Twitter user posts after having manually annotated 6400 tweets mentioning these three medications.…”