Discovering Urban Governance Parameters for Online Learning in Saudi Arabia During COVID-19 Using Topic Modeling of Twitter Data

Abstract

Smart cities are a relatively recent phenomenon that has rapidly grown in the last decade due to several political, economic, environmental, and technological factors. Data-driven artificial intelligence is becoming so fundamentally ingrained in these developments that smart cities have been called artificially intelligent cities and autonomous cities. The COVID-19 pandemic has increased the physical isolation of people and consequently escalated the pace of human migration to digital and virtual spaces. This paper investigates the use of AI in urban governance as to how AI could help governments learn about urban governance parameters on various subject matters for the governments to develop better governance instruments. To this end, we develop a case study on online learning in Saudi Arabia. We discover ten urban governance parameters using unsupervised machine learning and Twitter data in Arabic. We group these ten governance parameters into four governance macro-parameters namely Strategies and Success Factors, Economic Sustainability, Accountability, and Challenges. The case study shows that the use of data-driven AI can help the government autonomously learn about public feedback and reactions on government matters, the success or failure of government programs, the challenges people are facing in adapting to the government measures, new economic, social, and other opportunities arising out of the situation, and more. The study shows that the use of AI does not have to necessarily replace humans in urban governance, rather governments can use AI, under human supervision, to monitor, learn and improve decision-making processes using continuous feedback from the public and other stakeholders. Challenges are part of life and we believe that the challenges humanity is facing during the COVID-19 pandemic will create new economic, social, and other opportunities nationally and internationally.

Sustainable Participatory Governance: Data-Driven Discovery of Parameters for Planning Online and In-Class Education in Saudi Arabia During COVID-19

“…The architecture's modules will be presented in Sections Data Collection and Storage, Data Preprocessing, Parameters Discovery (Education Governance), Validation, and Visualization and Reporting. Additional details about the general methodology related to this study can be found in the study of Alswedani et al (2022).…”

“…This work is an extension of our earlier work that had considered 128 thousand tweets for a 66-days period from October 2020 to December 2020 (Alswedani et al, 2022). The work presented in this study makes substantial improvements to the data collection process and overall methodology, including the collection of around 2 million tweets for a period of 14 months (October 2020-December 2021).…”

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

“…Zhou et al [31] provided a cross-sectional study that investigates the prevalence rates of depression and anxiety, as well as their socio-demographic correlation, among Chinese high school students between 12-18 years who got affected by the COVID-19 pandemic. Alswedani et al also reported evidence for psychological stress among students, educators, and parents during COVID-19 [32], [33]. Other studies that reported psychological effects of COVID-19 include [34], [35].…”

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