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Big data directed acyclic graph model for real-time COVID-19 twitter stream detection.

Bakhtiar Amen1, Syahirul Faiz2, Thanh-Toan Do3

  • 1Department of Computer Science, School of Electrical Engineering, Electronics, and Computer Science, University of Liverpool, Liverpool L69 3BX, UK.

Pattern Recognition
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This study introduces a new framework to detect anomalous COVID-19 events from Twitter data. The system identifies key terms and public concerns during the pandemic, offering real-time insights.

Keywords:
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Area of Science:

  • Data Science
  • Computational Social Science
  • Epidemiology

Background:

  • Social media platforms like Twitter generate vast real-time data streams.
  • Twitter served as a critical communication channel for leaders and scientists during the COVID-19 pandemic.
  • Public concerns regarding virus spread and mortality were widely shared on Twitter.

Purpose of the Study:

  • To detect anomalous events related to COVID-19 using Twitter data.
  • To develop a system for processing and analyzing large-scale, real-time tweets about COVID-19.
  • To identify, cluster, and visualize significant keywords and public concerns.

Main Methods:

  • A distributed Directed Acyclic Graph (DAG) topology framework was proposed.
  • A novel lightweight algorithm was developed for automatic anomaly detection.
  • Keyword identification, clustering, and visualization techniques were employed.

Main Results:

  • The system detected the highest anomaly on August 18, 2020, correlating with casualty updates and pandemic debates.
  • Top terms included "covid", "death", and "Trump".
  • Keywords such as "people", "school", and "virus" had high TF-IDF scores, indicating significant discussion.

Conclusions:

  • The developed framework effectively processes real-time Twitter data for anomaly detection.
  • Keyword clustering revealed key public concerns, grouping terms like "death", "corona", "case" and "pandemic", "school", "president".
  • The system provides valuable insights into public sentiment and critical topics during the COVID-19 pandemic.