ArticleJournal of medical Internet research2023
Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Methods for Detecting Suspicious Information From Individual Transactions of Pharmaceutical Products via Twitter (now X): Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Bi-objective location-allocation model of interventions in high drug consumption areas incorporating X topic modeling.Health care management science · 2026Article
- Clinical insights: A comprehensive review of language models in medicine.PLOS digital health · 2025Review
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- AI and Big Data approaches to addressing the opioid crisis: a scoping review protocol.BMJ open · 2024Article
- One size fits all: Enhanced zero-shot text classification for patient listening on social media.Frontiers in artificial intelligence · 2024Article
- Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine.Frontiers in psychiatry · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA hallmark of unregulated drug markets is their unpredictability and constant evolution with newly introduced substances. People who use drugs and the public health workforce are often unaware of the appearance of new drugs on the unregulated market and their type, safe dosage, and potential adverse effects. This increases risks to people who use drugs, including the risk of unknown consumption and unintentional drug poisoning. Early warning systems (EWSs) can help monitor the landscape of emerging drugs in a given community by collecting and tracking up-to-date information and determining trends. However, there are currently few ways to systematically monitor the appearance and harms of new drugs on the unregulated market in Canada.
objectiveThe goal of this work is to examine how artificial intelligence can assist in identifying patterns of drug-related risks and harms, by monitoring the social media activity of public health and law enforcement groups. This information is beneficial in the form of an EWS as it can be used to identify new and emerging drug trends in various communities.
methodsTo collect data for this study, 145 relevant Twitter accounts throughout Quebec (n=33), Ontario (n=78), and British Columbia (n=34) were manually identified. Tweets posted between August 23 and December 21, 2021, were collected via the application programming interface developed by Twitter for a total of 40,393 tweets. Next, subject matter experts (1) developed keyword filters that reduced the data set to 3746 tweets and (2) manually identified relevant tweets for monitoring and early warning efforts for a total of 464 tweets. Using this information, a zero-shot classifier was applied to tweets from step 1 with a set of keep (drug arrest, drug discovery, and drug report) and not-keep (drug addiction support, public safety report, and others) labels to see how accurately it could extract the tweets identified in step 2.
resultsWhen looking at the accuracy in identifying relevant posts, the system extracted a total of 584 tweets and had an overlap of 392 out of 477 (specificity of ~84.5%) with the subject matter experts. Conversely, the system identified a total of 3162 irrelevant tweets and had an overlap of 3090 (sensitivity of ~94.1%) with the subject matter experts.
conclusionsThis study demonstrates the benefits of using artificial intelligence to assist in finding relevant tweets for an EWS. The results showed that it can be quite accurate in filtering out irrelevant information, which greatly reduces the amount of manual work required. Although the accuracy in retaining relevant information was observed to be lower, an analysis showed that the label definitions can impact the results significantly and would therefore be suitable for future work to refine. Nonetheless, the performance is promising and demonstrates the usefulness of artificial intelligence in this domain.
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