Emotions of COVID-19: A Study of Self-Reported Information and Emotions during the COVID-19 Pandemic using Artificial Intelligence.

BACKGROUND The COVID-19 pandemic has disrupted human societies across the world. Starting with a public health emergency, followed by a significant loss of human life, and the ensuing social restrictions leading to loss of employment, lack of interactions and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental wellbeing of all individuals impacted by the pandemic. OBJECTIVE This research aims to investigate the human emotions of the COVID-19 pandemic expressed on social media over time, using an Artificial Intelligence framework. METHODS Our study explores emotion classifications, intensities, transitions, profiles and alignment to key themes and topics, across the four stages of the pandemic; declaration of a global health crisis, first lockdown, easing of restrictions, and the second lockdown. This study employs an artificial intelligence framework comprising of natural language processing, word embeddings, Markov models and Growing Self-Organizing Maps that are collectively used to investigate the social media conversations. The investigation was carried out using 73,000 public Twitter conversations from users in Australia from January to September 2020. RESULTS The outcomes of this study enabled us to analyse and visualise different emotions and related concerns expressed, reflected on social media during the COVID-19 pandemic, that can be used to gain insights on citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people have expressed during the four stages of the pandemic. It was noted that starting from personal level concerns, the concerns expressed over social media has escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that 'fear' and 'sad' emotions were more prominently expressed at first, however, they transition into 'anger' and 'disgust' over time. Negative emotions except 'sad' were significantly higher (P < .05) in the second lockdown showing increased frustration. The temporal emotion analysis was conducted by modelling the emotion state changes across the four stages which demonstrated how different emotions emerge and shift over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles. CONCLUSIONS This study showed diverse emotions and concerns expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study establishes the use of social media to discover informed insights during a time where physical communication is impossible, the outcomes also contribute towards post-pandemic recovery, understanding psychological impact via emotion changes and potentially informing healthcare decision-making. The study exploits AI and social media to enhance our understanding of human behaviours in global emergencies, leading to improved planning and policymaking for future crises. CLINICALTRIAL