Exploring the surge of negativity during the COVID-19 pandemic: computational text and sentiment analysis across eight newsrooms' tweets.
The rise of Twitter as a news platform has radically changed the way we access, consume, and share news. Twitter becomes an important hub to quickly and easily access accurate information in times of crisis such as COVID-19 and is frequently used in journalism practices. This study examines how the...
| Publicado en: | Atlantic Journal of Communication Vol. 32; no. 2; pp. 298 - 325 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=175302366&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 175302366 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 15456870 U8J jtl: Atlantic Journal of Communication issn: 15456870 maglogo: Y pubinfo: dt: 2024 vid: 32 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 175302366 10.1080/15456870.2023.2293169 ppf: 298 ppct: 27 formats: fmt: – @attributes: type: T – @attributes: type: P size: 22.9MB tig: atl: Exploring the surge of negativity during the COVID-19 pandemic: computational text and sentiment analysis across eight newsrooms' tweets. aug: au: Kahraman-Gokalp, Elif Demirel, Sadettin Gündüz, Uğur affil: Department of Corporate Communications, Istanbul University, Istanbul, Türkiye Faculty of Communication, Department of New Media and Communication, Uskudar University, Istanbul, Türkiye Faculty of Communication, Department of Journalism, Istanbul University, Istanbul, Türkiye su: COVID-19 pandemic X Corp. Sentiment analysis Al Jazeera English (Television network) Associated Press Text mining News agencies Sadness Newsrooms sug: subj: COVID-19 pandemic X Corp. Sentiment analysis Al Jazeera English (Television network) Associated Press Text mining News agencies Sadness Newsrooms ab: The rise of Twitter as a news platform has radically changed the way we access, consume, and share news. Twitter becomes an important hub to quickly and easily access accurate information in times of crisis such as COVID-19 and is frequently used in journalism practices. This study examines how the COVID-19 pandemic is covered by news agencies in the Twitter ecosystem, the weight of the news about the pandemic in tweets and the sentiment analysis of the news. Within the scope of the study, the tweets related to COVID-19 shared between 2020 and 2021 by eight news agencies (BBC World, Reuters, CNN, Associated Press, TRT World, AL Jazeera English, DW English, Euronews) that broadcast on a global scale and have a high number of followers on Twitter are analyzed by using text mining methods. Firstly, the frequently used words in tweets were obtained by using the text analysis technique n-gram. Secondly, the sentiment values of all the tweets and the words are computed and later classified into certain categories. Lexicon based sentiment dictionaries such as VADER and NRC utilized in the sentiment analysis process. Findings reveal that messages containing fear, anxiety, sadness, and negative polarity are prevalent in the news during the pandemic. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Atlantic Journal of Communication is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Atlantic Journal of Communication holder: Taylor & Francis Ltd dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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