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Related Experiment Video

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An Infoveillance System for Detecting and Tracking Relevant Topics From Italian Tweets During the COVID-19 Event.

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  • 1Department of Information Engineering, Electronics and TelecommunicationsUniversity of Rome "La Sapienza," 00184 Rome Italy.

IEEE Access : Practical Innovations, Open Solutions
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Summary

This study analyzed Italian Twitter data during the COVID-19 lockdown, identifying key topics and sociopolitical events using NLP and graph analysis. The findings highlight the platform's role in public discourse during the pandemic.

Keywords:
COVID-19Natural language processinginfodemiologyinfoveillancesocial network analysistext miningtopic detectiontopic tracking

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Area of Science:

  • Computational Social Science
  • Natural Language Processing
  • Epidemiology

Background:

  • The COVID-19 pandemic caused widespread societal disruption and government restrictions globally.
  • Online social media, particularly Twitter, became a primary source of information and public debate during the lockdown.
  • Italy experienced severe impacts, necessitating an analysis of its digital public sphere during this period.

Purpose of the Study:

  • To analyze emergent topics and sociopolitical discussions within the Italian Twitter community during the COVID-19 lockdown.
  • To develop and validate a methodological framework for tracking trending topics on social media.
  • To understand public discourse patterns in response to a major global health crisis.

Main Methods:

  • A methodological framework combining natural language processing (NLP) and graph analysis techniques was employed.
  • Term-frequency analysis across time slots, incorporating user social influence and tweet quality metrics.
  • Co-occurrence analysis to construct a topic graph for identifying and selecting emergent themes.

Main Results:

  • The study successfully identified and tracked key emergent topics discussed on the Italian Twitter platform during the lockdown.
  • The developed system demonstrated effectiveness in capturing significant sociopolitical events as reflected in online discussions.
  • Analysis highlighted the dynamics of public discourse shaped by the pandemic and lockdown measures.

Conclusions:

  • The proposed topic tracking system, adapted for Twitter API limitations, effectively captures public discourse during crises.
  • Social media platforms serve as crucial indicators of societal concerns and reactions during major events like pandemics.
  • The findings offer insights into the intersection of public health crises, social media, and sociopolitical communication.