mlsanityData to 14 Oct 2026

Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection

arXiv 2111.07819 ↗First posted 15 Nov 2021cs.CL cs.AI cs.LGcited by 10

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Abstract (arXiv)

A drastic rise in potentially life-threatening misinformation has been a by-product of the COVID-19 pandemic. Computational support to identify false information within the massive body of data on the topic is crucial to prevent harm. Researchers proposed many methods for flagging online misinformation related to COVID-19. However, these methods predominantly target specific content types (e.g., news) or platforms (e.g., Twitter). The methods' capabilities to generalize were largely unclear so far. We evaluate fifteen Transformer-based models on five COVID-19 misinformation datasets that include social media posts, news articles, and scientific papers to fill this gap. We show tokenizers and models tailored to COVID-19 data do not provide a significant advantage over general-purpose ones. Our study provides a realistic assessment of models for detecting COVID-19 misinformation. We expect that evaluating a broad spectrum of datasets and models will benefit future research in developing misinformation detection systems.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 28 of its 38 references that are in the map. First posted on arXiv 15 Nov 2021.

Builds on

28 of 38 references.

References · 28

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16 more
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Cited by year

Citing papers per year: 2022: 7; 2023: 2; 2024: 0; 2025: 1.

10 papers.

  1. 2023ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languagescited by 34
  2. 2022A Domain-adaptive Pre-training Approach for Language Bias Detection in Newscited by 16
  3. 2022Specialized Document Embeddings for Aspect-based Similarity of Research Paperscited by 12
  4. 2022MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detectioncited by 7
  5. 2022Evaluating Generalizability of Fine-Tuned Models for Fake News Detectioncited by 3
  6. 2022Analyzing Multi-Task Learning for Abstractive Text Summarizationcited by 3
  7. 2022CS-Insights: A System for Analyzing Computer Science Researchcited by 2
  8. 2022Time-Aware Datasets are Adaptive Knowledgebases for the New Normalcited by 1
  9. 2023Continuously Reliable Detection of New-Normal Misinformation: Semantic Masking and Contrastive Smoothing in High-Density Latent Regionscited by 0
  10. 2025Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval modelscited by 0