ArticleNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2019
Identifying substance use risk based on deep neural networks and Instagram social media data.
Article in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current reporting of usability and impact of mHealth interventions for substance use disorder: A systematic review.Drug and alcohol dependence · 2020Pooled it
- The Use of Machine Learning to Predict Offline Adolescent e-Cigarette Use: a Proof-of-Concept.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Week-Ahead Prediction of High-Risk Drinking Episodes Among Young Adults Using Wearable Biosignals and Psychological Vulnerabilities: Prospective Observational Machine Learning Study.JMIR mHealth and uHealth · 2026Observational
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Predicting substance use behaviors with machine learning using small sets of judgment and contextual variables.Npj mental health research · 2026Article
- Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Youth by Using Mobile Phone Data: Development and Validation Study.Online journal of public health informatics · 2025Article
- Associations between posting about alcohol on social networking sites and alcohol-induced blackouts in a sample of young adults not in 4-year college.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025Article
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Article
- The synergistic effect of artificial intelligence technology in the evolution of visual communication of new media art.Heliyon · 2024Article
- Evaluating the Efficacy of a Digital Therapeutic (CT-152) as an Adjunct to Antidepressant Treatment in Adults With Major Depressive Disorder: Protocol for the MIRAI Remote Study.JMIR research protocols · 2024Article
- Navigating the Modern Landscape of Social Media: Ethical Considerations for Research With Adolescents and Young Adults.Translational issues in psychological science · 2024Article
- Detecting Substance Use Disorder Using Social Media Data and the Dark Web: Time- and Knowledge-Aware Study.JMIRx med · 2024Article
- Twitter users perceptions of AI-based e-learning technologies.Scientific reports · 2024Article
- Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study.JMIR formative research · 2024Article
- Digital interventions targeting excessive substance use and substance use disorders: a comprehensive and systematic scoping review and bibliometric analysis.Frontiers in psychiatry · 2024Article
- Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder.Journal of medical Internet research · 2023Article
- Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data.Scientific reports · 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
- Opioid death projections with AI-based forecasts using social media language.NPJ digital medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Social media may provide new insight into our understanding of substance use and addiction. In this study, we developed a deep-learning method to automatically classify individuals' risk for alcohol, tobacco, and drug use based on the content from their Instagram profiles. In total, 2287 active Instagram users participated in the study. Deep convolutional neural networks for images and long short-term memory (LSTM) for text were used to extract predictive features from these data for risk assessment. The evaluation of our approach on a held-out test set of 228 individuals showed that among the substances we evaluated, our method could estimate the risk of alcohol abuse with statistical significance. These results are the first to suggest that deep-learning approaches applied to social media data can be used to identify potential substance use risk behavior, such as alcohol use. Utilization of automated estimation techniques can provide new insights for the next generation of population-level risk assessment and intervention delivery.
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Registered trials
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.