Understanding the Complex Nexus between Government Stringencies and Adverse Public Sentiments and Misinformation in Social Media: Effects on Pandemic Preparedness and Response

Abstract

The COVID-19 pandemic highlighted the need for surveillance approaches that account for both epidemiological trends and the online information environment. Government policies, misinformation, and public sentiment evolved alongside pandemic conditions, potentially providing complementary information for forecasting. This study examined whether lagged government policy indicators and social-media signals improved short-term forecasts of COVID-19 cases and deaths in Canada beyond recent epidemiological history. An ecological time-series study integrated national COVID-19 outcomes, twelve Oxford COVID-19 Government Response Tracker policy indicators, and a corpus of 5,971 Reddit posts from January 2020 through December 2021. A stratified sample was manually annotated for misinformation and negative sentiment. DeBERTa-v3 and RoBERTa classifiers were fine-tuned to generate post-level misinformation and sentiment predictions, while a BART-based zero-shot classifier characterized discussion topics. Social-media indicators were aggregated at weekly and biweekly resolutions. Separate XGBoost models predicted cases and deaths using four predictor configurations: epidemiological indicators alone, epidemiological and policy indicators, epidemiological and social-media indicators, and all predictor groups combined. Models were evaluated using chronological training, validation, and test partitions. SHapley Additive exPlanations (SHAP) characterized feature contributions. The misinformation classifier demonstrated high precision but limited sensitivity, while the sentiment classifier showed stronger ranking performance than its threshold-based classification results. Vaccine discussions and lockdowns and restrictions dominated the selected topic-analysis subset. Forecasting performance varied by outcome and temporal resolution. Weekly mortality forecasting showed the clearest improvements from additional policy and social-media indicators. For biweekly mortality, the epidemiological-plus-policy model performed best, and adding social-media indicators did not improve performance further. Case forecasting remained predominantly dependent on recent epidemiological history, with inconsistent improvements for weekly cases and poorer performance from augmented models for biweekly cases. Although social-media features ranked highly in some SHAP analyses, their importance within fitted models did not consistently translate into improved test-set accuracy. These findings suggest that policy and Reddit-derived social-media indicators may complement conventional epidemiological surveillance, particularly for mortality forecasting. However, the small temporal samples, selective Reddit coverage, and annotation and classification uncertainty limit generalizability. The results represent retrospective predictive associations rather than causal effects or demonstrated operational early-warning capability. Further evaluation across forecast periods, geographic settings, and more representative data is needed to establish their practical value for pandemic preparedness and response.