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Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis.

Amina Amara1, Mohamed Ali Hadj Taieb2, Mohamed Ben Aouicha2

  • 1Multimedia, InfoRmation systems and Advanced Computing Laboratory, University of Sfax, Sfax, Tunisia.

Applied Intelligence (Dordrecht, Netherlands)
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Summary
This summary is machine-generated.

This study analyzes Facebook public posts in seven languages to track COVID-19 trends and public health measures from January to May 2020. Findings reveal how pandemic information evolved across different countries and languages.

Keywords:
Covid-19Data visualizationFacebookMultilingualSocial media analysisTopic modeling

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Area of Science:

  • Public Health
  • Data Science
  • Computational Social Science

Background:

  • Social media data is valuable for disaster monitoring and risk management.
  • Previous research on COVID-19 trend analysis primarily utilized Twitter data.
  • Facebook, a less-explored social media platform, offers a rich source for pandemic-related insights.

Purpose of the Study:

  • To analyze COVID-19 trends and public discourse using rarely exploited Facebook data.
  • To develop and apply an analytics process for tracking pandemic evolution across multiple languages.
  • To compare the chronological development of pandemic information and public health measures globally.

Main Methods:

  • Extraction of a multilingual dataset from public Facebook posts across seven languages (EN, AR, ES, IT, DE, FR, JP).
  • Application of an analytics process involving data gathering, pre-processing, Latent Dirichlet Allocation (LDA)-based topic modeling, and graph-based visualization.
  • Analysis of data covering January 1st, 2020, to May 15th, 2020, segmented into three cumulative periods.

Main Results:

  • The LDA topic modeling successfully identified key themes and discussions related to the COVID-19 pandemic.
  • Extracted topics accurately reflected the chronological progression of pandemic-related information and public health interventions.
  • Analysis revealed language-specific and country-specific trends in the discourse surrounding the pandemic.

Conclusions:

  • Facebook public posts provide a valuable, underutilized resource for monitoring global public health trends like COVID-19.
  • The proposed analytics framework effectively captures the dynamic evolution of pandemic-related information across diverse linguistic and cultural contexts.
  • This research highlights the importance of multilingual social media analysis for understanding and managing global health crises.