Author: Loutfi, M.; Tibau, M.; Siqueira, S. W. M.; Nunes, B. P.
Title: CovidTrends: Identifying behaviors during the COVID-19 pandemic: An analysis based on Google trends and news Cord-id: ojgdnwc2 Document date: 2021_1_1
ID: ojgdnwc2
Snippet: This paper presents the identification of people's behavioral changes during the Covid-19 pandemic period by analyzing the terms searched on Google's Trend and news. We developed an artifact, called CovidTrends, used the DSR (Design Science Research) epistemological approach, the Design Science Research Methodology (DSRM), and the document analysis method to list infodemic or people's behavioral trending of interest on Google and correlating them to news within the timeframe in which the terms'
Document: This paper presents the identification of people's behavioral changes during the Covid-19 pandemic period by analyzing the terms searched on Google's Trend and news. We developed an artifact, called CovidTrends, used the DSR (Design Science Research) epistemological approach, the Design Science Research Methodology (DSRM), and the document analysis method to list infodemic or people's behavioral trending of interest on Google and correlating them to news within the timeframe in which the terms' queries peaked. CovidTrends enabled the identification of three main behaviors, which we verified on news reporting in the media. Then, it proves to be appropriate to support data analysis and identify people's pandemic behavior. © 2021 ACM.
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