ArticleInternational journal of environmental research and public health2022
Fine-Tuning BERT Models to Classify Misinformation on Garlic and COVID-19 on Twitter.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Evaluating Public Sentiment on Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Compared With Other Mental Health Disorders From Posts on X (Formerly Known as Twitter): Longitudinal Analysis.JMIR infodemiology · 2026Article
- Unveiling Social Media Content Related to ADHD Treatment: Machine Learning Study Using X's Posts over 15 Years.Healthcare (Basel, Switzerland) · 2025Article
- Transformer-based models for ADR detection: cross-drug validation and benchmarking against large language models.Therapeutic advances in drug safety · 2025Article
- Article
- Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.PloS one · 2024Article
- The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection.JMIR infodemiology · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (BERT) can be used to classify misinformation from a vast number of tweets. This study aimed to apply the BERT model for classifying misinformation on garlic and COVID-19 on Twitter, using 5929 original tweets mentioning garlic and COVID-19 (4151 for fine-tuning, 1778 for test). Tweets were manually labeled as 'misinformation' and 'other.' We fine-tuned five BERT models (BERT
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