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Around the world in 60 days: an exploratory study of impact of COVID-19 on online global news sentiment

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  • Amartya Chakraborty

    (Jadavpur University)

  • Sunanda Bose

    (Jadavpur University)

Abstract

The world is going through an unprecedented crisis due to COVID-19 breakout, and people all over the world are forced to stay indoors for safety. In such a situation, the rise and fall of the number of affected cases or deaths has turned into a constant headline in most news channels. Consequently, there is a lack of positivity in the world-wide news published in different forms of media. Texts based on news articles, movie reviews, tweets, etc. are often analyzed by researchers, and mined for determining opinion or sentiment, using supervised and unsupervised methods. The proposed work takes up the challenge of mining a comprehensive set of online news texts, for determining the prevailing sentiment in the context of the ongoing pandemic, along with a statistical analysis of the relation between actual effect of COVID-19 and online news sentiment. The amount and observed delay of impact of the ground truth situation on online news is determined on a global scale, as well as at country level. The authors conclude that at a global level, the news sentiment has a good amount of dependence on the number of new cases or deaths, while the effect varies for different countries, and is also dependent on regional socio-political factors.

Suggested Citation

  • Amartya Chakraborty & Sunanda Bose, 2020. "Around the world in 60 days: an exploratory study of impact of COVID-19 on online global news sentiment," Journal of Computational Social Science, Springer, vol. 3(2), pages 367-400, November.
    • Handle: RePEc:spr:jcsosc:v:3:y:2020:i:2:d:10.1007_s42001-020-00088-3
    DOI: 10.1007/s42001-020-00088-3
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    References listed on IDEAS

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    1. Wataru Souma & Irena Vodenska & Hideaki Aoyama, 2019. "Enhanced news sentiment analysis using deep learning methods," Journal of Computational Social Science, Springer, vol. 2(1), pages 33-46, January.
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    Cited by:

    1. Emilio Ferrara & Stefano Cresci & Luca Luceri, 2020. "Misinformation, manipulation, and abuse on social media in the era of COVID-19," Journal of Computational Social Science, Springer, vol. 3(2), pages 271-277, November.
    2. Waseem Ahmad & Bang Wang & Philecia Martin & Minghua Xu & Han Xu, 2023. "Enhanced sentiment analysis regarding COVID-19 news from global channels," Journal of Computational Social Science, Springer, vol. 6(1), pages 19-57, April.
    3. Dylong, Patrick & Koenings, Fabian, 2023. "Framing of economic news and policy support during a pandemic: Evidence from a survey experiment," European Journal of Political Economy, Elsevier, vol. 76(C).
    4. Ryuichi Saito & Shinichiro Haruyama, 2023. "Estimating time-series changes in social sentiment @Twitter in U.S. metropolises during the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 6(1), pages 359-388, April.

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