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Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework.

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Artificial intelligence can help identify new drug trends by monitoring social media, aiding early warning systems (EWSs). This AI approach shows promise in reducing manual data review for public health surveillance.

Keywords:
CanadaFleissTwitteraddictioncommunitydevelopmentdosagedrugearly warning systemlaw enforcementmachine learningmonitoringnew psychoactive substancespharmacologypoisoningpublic healthpublic safetysocial mediatweettweet annotations

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Area of Science:

  • Public Health
  • Data Science
  • Artificial Intelligence

Background:

  • Unregulated drug markets are unpredictable, posing risks due to unknown substances and dosages.
  • Emerging drug trends and harms are difficult to track, increasing risks of poisoning.
  • Existing early warning systems (EWSs) have limited capacity for systematic monitoring in Canada.

Purpose of the Study:

  • To explore the use of artificial intelligence (AI) in identifying drug-related risks and harms.
  • To monitor social media activity of public health and law enforcement for emerging drug trends.
  • To develop an AI-assisted EWS for community-level drug trend identification.

Main Methods:

  • Collected 40,393 tweets from 145 Twitter accounts across Quebec, Ontario, and British Columbia (Aug-Dec 2021).
  • Used subject matter experts to develop keyword filters, reducing data to 3746 tweets.
  • Applied a zero-shot classifier to identify relevant tweets for EWS monitoring.

Main Results:

  • AI classifier achieved ~84.5% specificity and ~94.1% sensitivity in identifying relevant tweets.
  • The system successfully filtered out a significant amount of irrelevant information.
  • Accuracy in retaining relevant information was lower, suggesting refinement of label definitions is needed.

Conclusions:

  • AI demonstrates significant potential in assisting early warning systems for drug trends.
  • AI can substantially reduce manual data analysis efforts in public health surveillance.
  • Further refinement of AI models and label definitions is recommended for optimal performance.