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Title:Examination of the Content of COVID-19-Related Tweets During the March-April 2020 Pandemic Peak
Author(s):Alasmari, Hanan; Zavalina, Oksana L.
Subject(s):COVID-19 in Social Media
Large Dataset Analysis
Data Mining
Natural Language Processing
Health Informatics
Abstract:As COVID-19 unfolded globally during the year of 2020, it led users around the world to react on social media. The study selected results of which are reported here is one of the first that focused on the topics discussed through COVID-19-related social media posts. By using computational tools, it identified and examined content of the tweets posted during the first global peak of the pandemic and identified terms most often used in them. It tested the pro-posed combination of data collection, processing, and text-mining analysis techniques for large-scale datasets that covers broader time spans than those commonly used with Tweeter data. Over a million of English-language tweets that included the keyword “COVID-19” anywhere and were posted from 58 different countries and territories around the world in March and April of 2020 were collected, processed and analyzed using several widely available Python tools. In addition to various health-related terms, the tweets most frequently contained spatial and temporal terms, as well as terms with the social focus which reflect the major concerns people and organizations felt in relation to this public health emergency. Findings are discussed, along with ideas for future research.
Issue Date:2021-03-17
Publisher:iSchools
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/109672
Rights Information:Copyright 2021 is held by Hanan Alasmari and Oksana L. Zavalina. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2021-03-19


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