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Textual analysis of a Twitter corpus during the COVID-19 pandemics

Author

Listed:
  • Valerio Astuti

    (Bank of Italy)

  • Marta Crispino

    (Bank of Italy)

  • Marco Langiulli

    (Bank of Italy)

  • Juri Marcucci

    (Bank of Italy)

Abstract

Text data gathered from social media are extremely up-to-date and have a great potential value for economic research. At the same time, they pose some challenges, as they require different statistical methods from the ones used for traditional data. The aim of this paper is to give a critical overview of three of the most common techniques used to extract information from text data: topic modelling, word embedding and sentiment analysis. We apply these methodologies to data collected from Twitter during the COVID-19 pandemic to investigate the influence the pandemic had on the Italian Twitter community and to discover the topics most actively discussed on the platform. Using these techniques of automated textual analysis, we are able to make inferences about the most important subjects covered over time and build real-time daily indicators of the sentiment expressed on this platform.

Suggested Citation

  • Valerio Astuti & Marta Crispino & Marco Langiulli & Juri Marcucci, 2022. "Textual analysis of a Twitter corpus during the COVID-19 pandemics," Questioni di Economia e Finanza (Occasional Papers) 692, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_692_22
    as

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    References listed on IDEAS

    as
    1. Simon Porcher & Thomas Renault, 2021. "Social distancing beliefs and human mobility: Evidence from Twitter," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    2. Brodeur, Abel & Clark, Andrew E. & Fleche, Sarah & Powdthavee, Nattavudh, 2021. "COVID-19, lockdowns and well-being: Evidence from Google Trends," Journal of Public Economics, Elsevier, vol. 193(C).
    3. Altig, Dave & Baker, Scott & Barrero, Jose Maria & Bloom, Nicholas & Bunn, Philip & Chen, Scarlet & Davis, Steven J. & Leather, Julia & Meyer, Brent & Mihaylov, Emil & Mizen, Paul & Parker, Nicholas &, 2020. "Economic uncertainty before and during the COVID-19 pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    4. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    5. Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205113, HAL.
    6. Margaret E. Roberts & Brandon M. Stewart & Edoardo M. Airoldi, 2016. "A Model of Text for Experimentation in the Social Sciences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 988-1003, July.
    7. Nicholas Beauchamp, 2017. "Predicting and Interpolating State‐Level Polls Using Twitter Textual Data," American Journal of Political Science, John Wiley & Sons, vol. 61(2), pages 490-503, April.
    8. Angelico, Cristina & Marcucci, Juri & Miccoli, Marcello & Quarta, Filippo, 2022. "Can we measure inflation expectations using Twitter?," Journal of Econometrics, Elsevier, vol. 228(2), pages 259-277.
    9. Edoardo M. Airoldi & Jonathan M. Bischof, 2016. "Improving and Evaluating Topic Models and Other Models of Text," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1381-1403, October.
    10. Hino, Airo & Fahey, Robert A., 2019. "Representing the Twittersphere: Archiving a representative sample of Twitter data under resource constraints," International Journal of Information Management, Elsevier, vol. 48(C), pages 175-184.
    11. Dave Altig & Scott Baker & Jose Maria Barrero & Nick Bloom & Philip Bunn & Scarlet Chen & Steven J Davis & Julia Leather & Brent Meyer & Emil Mihaylov & Paul Mizen & Nick Parker & Thomas Renault & Paw, 2020. "Economic uncertainty before and during the Covid-19 pandemic," Bank of England working papers 876, Bank of England.
    12. Ro'ee Levy, 2021. "Social Media, News Consumption, and Polarization: Evidence from a Field Experiment," American Economic Review, American Economic Association, vol. 111(3), pages 831-870, March.
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    More about this item

    Keywords

    text as data; Twitter; big data; sentiment; Covid-19; topic analysis; word embedding;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media

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