The evolution mechanism of social emotions during the Covid-19 pandemic: an analysis based on big data from Twitter and GDELT

Abstract

Abstract This paper focuses on the evolutionary mechanism of social emotions, such as panic, anxiety, and depression, during the COVID-19 pandemic by using big data from the internet, including Twitter and GDELT, and by incorporating factors related to risk communication and risk response in risk governance. The main negative emotions experienced during the COVID-19 pandemic include panic, anxiety, and depression. These negative emotions primarily erupted on a large scale during the initial phase of the first wave of the epidemic, whereas the fluctuations in negative emotions during subsequent resurgences were significantly smaller. This paper explains the evolution of panic and depression through analytical frameworks based on threat perception and coping efficacy, as well as social pressure and social support. The fluctuations in negative emotions around the world exhibit both similarities and diversities, which are closely related to the diversity of pandemic response models and cultural characteristics across different regions. The findings of this study have important implications for emergency management and the guidance of social mentality.

Keywords

• Big data
• Social emotions
• COVID-19 pandemic
• Twitter
• GDELT