Towards a Tweet Analysis System to Study Human Needs During COVID-19 Pandemic

Abstract

Governments and municipalities need to understand their citizens’ psychological needs in critical times and dangerous situations. COVID-19 brings lots of challenges to deal with. We propose NeedFull, an interactive and scalable tweet analysis platform, to help governments and municipalities to understand residents’ real psychological needs during those periods. The platform mainly consists of four parts: data collection module, data storage module, data analysis module and data visualization module. The whole process of how data flows in the system is illustrated as follows: Our crawlers in the data collection module gather raw data from a popular social network website Twitter. Then the data is fed into our human need detection model in the data analysis module before stored into the database. When a user enters a query through the user interface, they will get all the related items in the database by the index system of the data storage module and a comprehensive human needs analysis of these items is then presented and depicted in the data visualization module. We employed the proposed platform to investigate the reaction of people in four big regions including New York, Ottawa, Toronto and Montreal to the ongoing worldwide COVID-19 pandemic by collecting tweets posted during this period. The results show that the most pronounced human need in these tweets is relatedness with 51.32%, followed by autonomy with 22.56% and competence with 18.82%. And the percentages of tweets expressing frustration are larger than those of tweets expressing satisfaction for each psychological need in general.