ArticleAlcoholism, clinical and experimental research2022
Using Facebook language to predict and describe excessive alcohol use.
Article in Alcoholism, clinical and experimental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed, 12 citations in OpenAlex.
- Qualitative analysis of how U.S. college students construct their alcohol-related content identities via social media.Substance abuse treatment, prevention, and policy · 2025Article
- Systematic Evaluation of Auto-Encoding and Large Language Model Representations for Capturing Author States and Traits.Findings of ACL. ACL · 2025Article
- The Relationship Between Alcohol-Related Content on Social Media and Alcohol Outcomes in Young Adults: A Scoping Review.Alcohol research : current reviews · 2025Article
- How developments in natural language processing help us in understanding human behaviour.Nature human behaviour · 2024Review
- GPT is an effective tool for multilingual psychological text analysis.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data.Scientific reports · 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
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundAssessing risk for excessive alcohol use is important for applications ranging from recruitment into research studies to targeted public health messaging. Social media language provides an ecologically embedded source of information for assessing individuals who may be at risk for harmful drinking.
methodsUsing data collected on 3664 respondents from the general population, we examine how accurately language used on social media classifies individuals as at-risk for alcohol problems based on Alcohol Use Disorder Identification Test-Consumption score benchmarks.
resultsWe find that social media language is moderately accurate (area under the curve = 0.75) at identifying individuals at risk for alcohol problems (i.e., hazardous drinking/alcohol use disorders) when used with models based on contextual word embeddings. High-risk alcohol use was predicted by individuals' usage of words related to alcohol, partying, informal expressions, swearing, and anger. Low-risk alcohol use was predicted by individuals' usage of social, affiliative, and faith-based words.
conclusionsThe use of social media data to study drinking behavior in the general public is promising and could eventually support primary and secondary prevention efforts among Americans whose at-risk drinking may have otherwise gone "under the radar."
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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.