Challenges in Curating Real-Time Data During a Crisis: The Case of the COVID-19 Pandemic in Alberta - Tchoh, Bennett Kuwan; Barnard, Sara; Budac, Robert; Ingram, Katrina; Chartier, Ryan; Baumgart, Daniel C; Rockwell, Geoffrey (Digital studies, 2025)
In early 2020, the SARS-CoV-2 virus was recognized as a threat by most countries, and the risk of its spread was already being discussed. After the first cases were identified and the sudden onset of lockdowns, we realized the need to document Albertans' experience of the pandemic. This paper describes our real-time collection, beginning in March 2020, of the discourse surrounding the COVID-19 pandemic in Alberta. We quickly implemented a data collection plan that involved gathering textual data from various sources. We collected the text of public health briefings given by the Chief Medical Officer of Health (CMOH) and the premier of the province, tweets about the pandemic in Alberta, and news articles published in online journals. We conducted preliminary data analysis to assess the suitability of the data for statistical analysis. This included word cloud visualizations, correlation analysis, and Term Frequency-Inverse Document Frequency (TF-IDF) analysis. The results of the analysis were plausible, suggesting that the datasets are suitable for even more advanced data extraction and analysis. The data was deposited in the University of Alberta's Dataverse, an open-source repository, where our datasets have attracted significant interest. Judging by download numbers, the speeches of the premier and the CMOH have each been downloaded more than 2,600 times. We used Otter.ai, an online transcription software, to transcribe the speeches of the premier. The high interest in the speeches of government officials made us realize that more resources should have been allocated to ensure near-perfect transcriptions of the premier's speeches, as the premier was responsible for making the final decisions on pandemic-related policies in the province.