Document Type
Conference Paper
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE
Abstract
Easy and uncontrolled access to the Internet provokes the wide propagation of false information, which freely circulates in the Internet. Researchers usually solve the problem of fake news detection (FND) in the framework of a known topic and binary classification. In this paper we study possibilities of BERT-based models to detect fake news in news flow with unknown topics and four categories: true, semi-true, false and other. The object of consideration is the dataset CheckThat! Lab proposed for the conference CLEF-2022. The subjects of consideration are the models SBERT, RoBERTa, and mBERT. To improve the quality of classification we use two methods: the addition of a known dataset (LIAR), and the combination of several classes (true + semi-true, false + semi-true). The results outperform the existing achievements, although the state-of-the-art in the FND area is still far from practical applications.
DOI
https://doi.org/10.23919/FRUCT58615.2023.10143004.
Recommended Citation
Shushkevich, Elena; Cardiff, John; and Boldyreva, Anna, "Detection of Truthful, Semi-Truthful, False and Other News with Arbitrary Topics Using BERT-Based Models" (2023). Conference Papers. 10.
https://arrow.tudublin.ie/smrgcon/10
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Publication Details
https://ieeexplore.ieee.org/document/10143004/keywords#keywords
Shushkevich, E., Cardiff, J. & Boldyreva, A. (2023). Detection of Truthful, Semi-Truthful, False and Other News with Arbitrary Topics Using BERT-Based Models. 33rd Conference of Open Innovations Association (FRUCT), Zilina, Slovakia, 2023, pp. 250-256,
https://doi.org/10.23919/FRUCT58615.2023.10143004.