Model Generalization on COVID-19 Fake News Detection
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Some scholars have raised concerns regarding the generalizability of detection models.
“Despite the fruitful outcomes of modeling impact factors of misinformation and enhancing detection accuracy, some scholars have raised concerns regarding the generalizability of detection models [6,101].”
From Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023) · cites this paper as [6]
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Agarwal, A. (2023) · AIP conference proceedings · doi:10.1063/5.0153931
African Vulture Optimization with Deep Learning Based COVID-19 Fake News Detection Model
Ibrahim, K., Abed, Z., Alkhafaji, M. A., Alawadi, A. H., & Sheel, S. (2023) · International Conference on Engineering Technology and its Applications (IICETA) · doi:10.1109/iiceta57613.2023.10351418
The COVID-19 Infodemic: A Survey (Preprint)Preprint
Al-Qahtani, A. F., Abbas, H., Cresci, S., & Pietro, R. D. (2025) · doi:10.2196/preprints.71318
Srivastava, S., Sarkar, M. K., & Chakraborty, C. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-98498-3_20
Hadi, A. A., Hommadi, A., & Ismael, H. A. (2026) · Journal of Information Systems and Telecommunication (JIST) · doi:10.66224/jist.49506.14.54.142
- Authors
- Bang, Y., Ishii, E., Cahyawijaya, S., Ji, Z., & Fung, P.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Bang, Yejin
- Venue
- Communications in Computer and Information Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 128-140
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- OpenAlex
- W3157206169
- Cited by
- 52
- References
- 45
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 6 · 2022: 23 · 2021: 12
- FWCI
- 30.66
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Communications in computer and information science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Communication › Communication and COVID-19 Impact
- Keywords
- infodemic, model generalization, robust loss functions, transformer fine-tuning, training data quality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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