Abstract
Automatic curation of consumer-generated, opioid-related social media big data may enable real-time monitoring of the opioid epidemic in the United States. To develop and validate an automatic text-processing pipeline for geospatial and temporal analysis of opioid-mentioning social media chatter. This cross-sectional, population-based study was conducted from December 1, 2017, to August 31, 2019, and used more than 3 years of publicly available social media posts on Twitter, dated from January 1, 2012, to October 31, 2015, that were geolocated in Pennsylvania. Opioid-mentioning tweets were extracted using prescription and illicit opioid names, including street names and misspellings. Social media posts (tweets) (n = 9006) were manually categorized into 4 classes, and training and evaluation of several machine learning algorithms were performed. Temporal and geospatial patterns were analyzed with the best-performing classifier on unlabeled data. Pearson and Spearman correlations of county- and substate-level abuse-indicating tweet rates with opioid overdose death rates from the Centers for Disease Control and Prevention WONDER database and with 4 metrics from the National Survey on Drug Use and Health for 3 years were calculated. Classifier performances were measured through microaveraged F1 scores (harmonic mean of precision and recall) or accuracies and 95% CIs. A total of 9006 social media posts were annotated, of which 1748 (19.4%) were related to abuse, 2001 (22.2%) were related to information, 4830 (53.6%) were unrelated, and 427 (4.7%) were not in the English language. Yearly rates of abuse-indicating social media post showed statistically significant correlation with county-level opioid-related overdose death rates (n = 75) for 3 years (Pearson r = 0.451, P < .001; Spearman r = 0.331, P = .004). Abuse-indicating tweet rates showed consistent correlations with 4 NSDUH metrics (n = 13) associated with nonmedical prescription opioid use (Pearson r = 0.683, P = .01; Spearman r = 0.346, P = .25), illicit drug use (Pearson r = 0.850, P < .001; Spearman r = 0.341, P = .25), illicit drug dependence (Pearson r = 0.937, P < .001; Spearman r = 0.495, P = .09), and illicit drug dependence or abuse (Pearson r = 0.935, P < .001; Spearman r = 0.401, P = .17) over the same 3-year period, although the tests lacked power to demonstrate statistical significance. A classification approach involving an ensemble of classifiers produced the best performance in accuracy or microaveraged F1 score (0.726; 95% CI, 0.708-0.743). The correlations obtained in this study suggest that a social media-based approach reliant on supervised machine learning may be suitable for geolocation-centric monitoring of the US opioid epidemic in near real time.
Highlights
The problem of drug addiction and overdose has reached epidemic proportions in the United States, and it is largely driven by opioids, both prescription and illicit.[1]
A classification approach involving an ensemble of classifiers produced the best performance in accuracy or microaveraged F1 score (0.726; 95% CI, 0.708-0.743)
Meaning The findings suggest that automatic processing of social media data, combined with geospatial and temporal information, may provide close to real-time insights into the status and trajectory of the opioid epidemic
Summary
The problem of drug addiction and overdose has reached epidemic proportions in the United States, and it is largely driven by opioids, both prescription and illicit.[1]. More than 72 000 overdose-related deaths in the United States were estimated to have occurred in 2017, of which more than 47 000 (approximately 68%) involved opioids,[2] meaning that a mean of more than 130 people died each day from opioid overdoses, and approximately 46 of these deaths were associated with prescription opioids.[3]. Studies have suggested that the state-by-state variations in opioid overdose–related deaths are multifactorial but may be associated with differences in state-level policies and laws regarding opioid prescribing practices and population-level awareness or education regarding the risks and benefits of opioid use.[5]. Kolodny and Frieden[11] discussed some of the drawbacks of current monitoring strategies and suggested 10 federal-level steps for reversing the opioid epidemic, with improved monitoring or surveillance as a top priority
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