ArticleBMC medical informatics and decision making2021
Text classification models for the automatic detection of nonmedical prescription medication use from social media.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Automated Extraction of Mortality Information From Publicly Available Sources Using Large Language Models: Development and Evaluation Study.Journal of medical Internet research · 2025Article
- Monitoring the opioid epidemic via social media discussions.NPJ digital medicine · 2025Article
- Which social media platforms facilitate monitoring the opioid crisis?PLOS digital health · 2025Article
- "I Been Taking Adderall Mixing it With Lean, Hope I Don't Wake Up Out My Sleep": Harnessing Twitter to Understand Nonmedical Prescription Stimulant Use among Black Women and Men Subscribers.medRxiv : the preprint server for health sciences · 2024Article
- Task-Specific Transformer-Based Language Models in Health Care: Scoping Review.JMIR medical informatics · 2024Article
- Classification of Patients' Judgments of Their Physicians in Web-Based Written Reviews Using Natural Language Processing: Algorithm Development and Validation.Journal of medical Internet research · 2024Article
- Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach.Journal of medical Internet research · 2023Article
- Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework.Journal of biomedical informatics · 2023Article
- Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter?Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Automatic Detection of Intimate Partner Violence Victims from Social Media for Proactive Delivery of Support.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- #ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning.Health data science · 2023Article
- A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications.BMC digital health · 2023Article
- Barriers to opioid use disorder treatment: A comparison of self-reported information from social media with barriers found in literature.Frontiers in public health · 2023Article
- Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions.Infectious disease reports · 2022Article
- A review on Natural Language Processing Models for COVID-19 research.Healthcare analytics (New York, N.Y.) · 2022Review
- Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques.JMIR formative research · 2022Article
- Natural language model for automatic identification of Intimate Partner Violence reports from Twitter.Array (New York, N.Y.) · 2022Article
- Comparison of Pretraining Models and Strategies for Health-Related Social Media Text Classification.Healthcare (Basel, Switzerland) · 2022Article
- Signals of increasing co-use of stimulants and opioids from online drug forum data.Harm reduction journal · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundPrescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.
methodsWe experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class.
resultsOur proposed fusion-based model performs significantly better than the best traditional model (F
conclusionsBERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.