Detecting COVID-19 Health Misinformation Targeting Older Adults

Detecting COVID-19 health misinformation targeting older adults using TF-IDF, logistic regression, and BERT

Author

Bozhao Wang & Zirui Chen, University of Calgary

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Overview

Canada’s population aged 85 and older reached 861 000 in 2021 which is a 12 % increase since 2016. Now accounting for 2.3 % of all Canadians. Many of these seniors rely on social media for health advice but lack easy access to verified expertise, leaving them vulnerable to misleading COVID-19 claims. This project develops and compares two automated NLP classifiers:

• TF–IDF + Logistic Regression
• Fine‐tuned BERT

Both models excel on a labeled Twitter dataset but suffer dramatic performance drops on senior‐focused Reddit posts. We also employ SHAP for interpretability and LDA topic modeling to uncover key misinformation themes.

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Data

1. Raw tweets
   Constraint COVID-19 Fake News dataset (6 420 tweets)

2. Cleaned & split tweets
   Place preprocessed CSV in data/constraint_train_clean.csv

3. Senior‐focused Reddit
   Collected via PRAW from r/AskDocs & r/seniors
   High-confidence pseudo-labels (≥ 90 %) saved as data/reddit_pseudo_labels.csv

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License

MIT © 2025 Bozhao Wang & Zirui Chen