In this paper, we collect data from Twitter and conduct a linguistic analysis of the user tweets to understand the social and economic disruption caused by the COVID-19 pandemic. To better appreciate peoples' opinions and concerns with regards to the socio-economic conditions of addiction, mental health, unemployment and immigration, we collect data for a period of approximately 3 months in the beginning of the pandemic. We analyze the term and co-occurrence frequencies to identify the most commonly occurring words and bigrams in the discussion for each of the four categories. We conduct semantic role labeling to determine the action words in each category and then adopt a LSTM-based dependency parsing model to identify the main nouns linked with these action words. We then adopt a seeded topic modeling approach to automatically identify the main topics of discussion in each category. We finally conclude with a sentiment analysis of the tweets in each category to determine the overall sentiment associated with each category. Our fine-grained linguistic study unearths the difficulties experienced by the people (e.g., action verb need associated with nouns such as aid and assistance in the unemployment category). We also observe that the overall sentiment in the tweets is negative, driven by people experiencing the pains of job loss, deportation, and the difficulty in accessing programs and treatments related to addiction. Our analysis highlights the main challenges experienced by the people during the start of the COVID-19 crisis and lays the foundation for recognizing and developing the most pertinent public and social policies so as to minimize peoples' suffering in case of a future pandemic. © 2021 IEEE.
Understanding the Societal Disruption due to COVID-19 via User Tweets