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Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation
Tanvi Sharma1, Amer Farea1, Nadeesha Perera1
1Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
Heliyon
|March 8, 2024
Summary
Transformer language models effectively extract COVID-19 information from both PubMed and Reddit. These models can identify novel relations and detect misinformation, highlighting Reddit
Area of Science:
- Computational linguistics
- Medical informatics
- Public health
Background:
- The COVID-19 pandemic necessitated rapid knowledge acquisition for healthcare systems.
- Text data is an underutilized resource for extracting critical medical and biological information.
- Efficient methods are needed to process vast amounts of biomedical text.
Purpose of the Study:
- To extract COVID-19-related relations using transformer-based language models.
- To compare the performance of models like BERT and DistilBERT on PubMed and Reddit data.
- To assess the models' ability to detect novel relations and misinformation.
Main Methods:
- Utilized transformer-based language models, including Bidirectional Encoder Representations from Transformers (BERT) and DistilBERT.
- Extracted COVID-19 relations from both PubMed and Reddit datasets.
- Analyzed the performance of five distinct language models.
Main Results:
- PubMed and Reddit data contain surprisingly similar COVID-19 information.
- Transformer models successfully extracted entities and relations from both sources.
- Language models demonstrated capability in identifying novel relations and potential misinformation.
Conclusions:
- Reddit serves as a valuable, rapidly accessible data source during health crises.
- Transformer language models are effective tools for biomedical knowledge extraction.
- The ability to uncover unseen relations aids in identifying misinformation during pandemics.
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