Unveiling emotional contagion in COVID-19 misinformation: Computational analysis for public health crisis surveillance

Abstract

Objectives: During the early phase of the COVID-19 outbreak, misinformation spread rapidly, hindering effective health communication and fueling xenophobic violence. The politicization of health issues, along with the manipulation by social bots and astroturfing accounts, posed significant challenges. This study aims to investigate how misinformation spreads through social media, involving malicious actors like trolls and bots, and explores emotional contagion during public health crises. Methods: Using a computational methodology that combines semantic modeling, social network analysis, bot identification, emotion analysis, and time series analysis, the study analyzed over 700,000 tweets from February to July 2020. Results: The findings reveal that inauthentic actors amplified negative emotions, particularly among news and political actors, while positive emotions were less prominent. Astroturfing accounts acted as key nodes, perpetuating negative emotional contagion. Conclusion: This study provides a framework for monitoring emotional responses in public health crises, with findings applicable beyond COVID-19 to other public health emergencies.