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A topic modeling framework for spatio-temporal information management.

Mohsen Asghari1, Daniel Sierra-Sosa1, Adel S Elmaghraby1

  • 1Department of Computer Science and Engineering, University of Louisville, KY, USA.

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Summary
This summary is machine-generated.

This study presents a framework for real-time analysis of streaming data, like Twitter health messages. It effectively detects and tracks trending topics using advanced data processing and deep learning techniques.

Keywords:
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Area of Science:

  • Computational social science
  • Data science
  • Public health informatics

Background:

  • Analyzing dynamic, real-time data streams, such as social media messages, presents significant challenges due to conflicting information from diverse sources and timeframes.
  • Online topic detection and tracking require robust methods to manage and process high-velocity data.
  • The increasing volume of health-related discussions on platforms like Twitter necessitates effective analytical tools.

Purpose of the Study:

  • To introduce a comprehensive framework for managing, processing, analyzing, detecting, and tracking topics in streaming data.
  • To address the challenges of online topic detection using a novel model selector procedure with a hybrid indicator.
  • To enhance data quality and improve the accuracy of health-related tweet classification.

Main Methods:

  • Developed an automatic data processing pipeline with dual-level cleaning (regular and deep) incorporating meta-knowledge.
  • Employed deep learning and transfer learning techniques for classifying health-related tweets.
  • Integrated data visualization tools, including a US map display, for understanding topic trends over time and location.

Main Results:

  • The framework demonstrated high accuracy and an improved F1-Score in classifying health-related tweets.
  • The system successfully detected and tracked topics in real-time, achieving performance comparable to manual annotation.
  • Graphical display on a US map effectively illustrated emerging and changing topics across different locations and time periods.

Conclusions:

  • The proposed framework provides an effective solution for real-time topic detection and tracking in dynamic streaming data environments.
  • The hybrid approach, combining advanced data processing with deep learning, significantly enhances the analysis of social media data for public health surveillance.
  • The visualization component offers valuable insights into geographical and temporal trends of online discussions.