ArticleNeural computing & applications2021
Using weak supervision to generate training datasets from social media data: a proof of concept to identify drug mentions.
Article in Neural computing & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- 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
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Authors and funding
2 authors.
Funding
Abstract
Twitter has been a remarkable resource for research in pharmacovigilance in the last decade. Traditionally, rule- or lexicon-based methods have been utilized for automatically extracting drug tweets for human annotation. The process of human annotation to create labeled sets for machine learning models is laborious, time consuming and not scalable. In this work, we demonstrate the feasibility of applying weak supervision (noisy labeling) to select drug data, and build machine learning models using large amounts of noisy labeled data instead of limited gold standard labelled sets. Our results demonstrate the models built with large amounts of noisy data achieve similar performance than models trained on limited gold standard datasets, hence demonstrating that weak supervision helps reduce the need to rely on manual annotation, allowing more data to be easily labeled and useful for downstream machine learning applications, in this case drug mention identification.
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Registered trials
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