A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications

Abstract

Background Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications. Methods We collected Twitter data for four medications—fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication. Results The quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities. Conclusions NLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.

Which social media platforms facilitate monitoring the opioid crisis?

“…Of all shortlisted social media platforms, X (formerly Twitter) and Reddit were by far the most commonly used in existing literature. The topics of studies using these two platforms range from correlating opioid-related discussion volume and opioid-related overdose death rates (2,(7)(8)(9)(10)41,42,70,71), characterizing trends and themes in online discussion of OUD and OUD treatment (4,27,(37)(38)(39)(40)70,74,87,(103)(104)(105)(106)(107)(108)(109)(110)(111)(112)(113)(114)(115)(116)(117)(118)(119)(120)(121), and characterizing public sentiment towards the opioid epidemic generally (39,82,93,108,122,123). Many research groups have also created models to automatically identify posts on X and Reddit with discussion related to opioids.…”