Abstract
Given the widespread accessibility of content creation and sharing, false information proliferation is a growing concern. Researchers typically tackle fake news detection (FND) in specific topics using binary classification. Our study addresses a more practical FND scenario, analyzing a corpus with unknown topics through multiclass classification, encompassing true, false, partially false, and other categories. Our contribution involves: (1) exploring three BERT-based models—SBERT, RoBERTa, and mBERT; (2) enhancing results via ChatGPT-generated artificial data for class balance; and (3) improving outcomes using a two-step binary classification procedure. Our focus is on the CheckThat! Lab dataset from CLEF-2022. Our experimental results demonstrate a superior performance compared to existing achievements but FND’s practical use needs improvement within the current state-of-the-art.
Keywords
ChatGPT, fake news detection, mBERT, multiclass classification, SBERT, transformers, XLM-RoBERTa
DOI Link
Publication Date
2023-01-01
Publication Title
Inventions
Volume
8
Issue
5
Deposit Date
2026-03-12
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Shushkevich, E., Alexandrov, M., & Cardiff, J. (2023) 'Improving Multiclass Classification of Fake News Using BERT-Based Models and ChatGPT-Augmented Data', Inventions, 8(5). Available at: 10.3390/inventions8050112