ABSTRACT
COVID-19 pandemic has resulted in excess mortality globally and presented an unprecedented challenge to people's lives. Despite the benefits of getting a COVID-19 vaccine, there have been arguments against the available vaccines and vaccine hesitancy worldwide. In this work, we analyze the information published by the public on Reddit as a digital forum, using unsupervised natural language processing to discover useful insights from the collected data related to COVID-19 vaccines, and validate the results of our study using Google Trends. Our results show that the government's contributions to the vaccination process, vaccine side-effects, and opposition to vaccine mandate and lock-downs are the main concerns shared by the public on digital forums. Moreover, we provide our collected data publicly available for further infodemiology studies by researchers and practitioners. © 2022 IEEE.
ABSTRACT
With the exponential growth of social media platforms like Twitter, a seemingly vast amount of data has become available for mining to draw conclusions about various topics, including awareness systems requirements the exchange of health-related information on social media has been heralded as a new way to explore information-seeking behaviour during pandemics and design and develop awareness systems that address the public's information needs. Online datasets such as Twitter, Google Trends and Reddit have several advantages over traditional data sources, including real-Time data availability, ease of access, and reduced cost. In this paper, to explore the pandemic awareness systems (PAS), requirements, we utilize data from the large accessible database of tweets and Reddit's posts to explore the contextual patterns and temporal trends in Canadians' information-seeking behaviour during the COVID-19 pandemic. To validate our inferences and to understand how Google searches regarding COVID-19 were distributed throughout the course of the pandemic in Canada, we complement our Twitter and Reddit data with that collected through Google Trends, which tracks the popularity of specific search terms on Google. Our results show that Social media content contains useful technical information and can be used as a source to explore the requirements of pandemic awareness systems. © 2020 ACM.