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.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/41210 |
| Date | 13 October 2020 |
| Creators | Long, Zijian |
| Contributors | El Saddik, Abdulmotaleb |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.0193 seconds