Detecting Fine-Grained Emotions on Social Media during Major Disease Outbreaks: Health and Well-being before and during the COVID-19 Pandemic

Abstract

The COVID-19 pandemic has affected the whole world in various ways. One type of impact is that communication, work, interaction, a great part of our lives has moved online on various platforms, with some of the most popular being the social media ones. Another, arguably less visible impact, is the emotional impact. Detecting and understanding emotions is important, to better discern the emotional health and well-being of the global population. Thus, in this work, we use a social media platform (Twitter) to analyse emotions in detail. Our contribution is twofold: (1) we propose EmoBERT, a new emotion-based variant of the BERT transformer model, able to learn emotion representations and outperform the state-of-the-art; (2) we provide a fine-grained analysis of the pandemic's effect in a major location, London, comparing specific emotions (annoyed, anxious, empathetic, sad) before and during the epidemic.