ArticleData in brief2017
A corpus for mining drug-related knowledge from Twitter chatter: Language models and their utilities.
Article in Data in brief, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
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Who cites it
21 citing papers in PubMed.
- Domain-informed density extraction for robust mental health classification of long social media posts.Frontiers in artificial intelligence · 2026Article
- BERTopic_Teen: a multi-module optimization approach for short text topic modeling in adolescent health.Frontiers in public health · 2025Article
- 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
- Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.Database : the journal of biological databases and curation · 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
- Using weak supervision to generate training datasets from social media data: a proof of concept to identify drug mentions.Neural computing & applications · 2021Article
- LexExp: a system for automatically expanding concept lexicons for noisy biomedical texts.Bioinformatics (Oxford, England) · 2021Article
- Text classification models for the automatic detection of nonmedical prescription medication use from social media.BMC medical informatics and decision making · 2021Article
- Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines.Journal of medical Internet research · 2020Article
- Leveraging digital media data for pharmacovigilance.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Deep neural networks ensemble for detecting medication mentions in tweets.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter.JAMA network open · 2019Article
- RedMed: Extending drug lexicons for social media applications.Journal of biomedical informatics · 2019Article
- Towards Automating Location-Specific Opioid Toxicosurveillance from Twitter via Data Science Methods.Studies in health technology and informatics · 2019Article
- Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data.Yearbook of medical informatics · 2019Article
- An unsupervised and customizable misspelling generator for mining noisy health-related text sources.Journal of biomedical informatics · 2018Article
- Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter.Journal of biomedical informatics · 2018Article
- Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research.CPT: pharmacometrics & systems pharmacology · 2018Review
- Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.Yearbook of medical informatics · 2017Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In this data article, we present to the data science, natural language processing and public heath communities an unlabeled corpus and a set of language models. We collected the data from Twitter using drug names as keywords, including their common misspelled forms. Using this data, which is rich in drug-related chatter, we developed language models to aid the development of data mining tools and methods in this domain. We generated several models that capture (i) distributed word representations and (ii) probabilities of n-gram sequences. The data set we are releasing consists of 267,215 Twitter posts made during the four-month period-November, 2014 to February, 2015. The posts mention over 250 drug-related keywords. The language models encapsulate semantic and sequential properties of the texts.
Identifiers
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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.