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NSLPCD: Topic based tweets clustering using Node significance based label propagation community detection algorithm.

Jagrati Singh1, Anil Kumar Singh1

  • 1CSED, Motilal Nehru National Institute of Technology Prayagraj, Prayagraj, India.

Annals of Mathematics and Artificial Intelligence
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PubMed
Summary
This summary is machine-generated.

This study introduces a faster topic detection method for social media, analyzing keywords to identify and cluster topics efficiently. The Node Significance based Label Propagation Community Detection (NSLPCD) algorithm improves both speed and accuracy in real-time information analysis.

Keywords:
Keyword co-occurrenceLabel propagationSupervised and Unsupervised techniqueTopic modelingTweet clustering

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Area of Science:

  • Computer Science
  • Data Science
  • Social Media Analysis

Background:

  • Social networks facilitate rapid information dissemination, leading to challenges in topic detection accuracy and scalability.
  • Existing topic detection methods are effective for popular topics but lack speed for real-time analysis.
  • The rapid spread of information on platforms like Twitter and Facebook necessitates faster and more accurate topic detection.

Purpose of the Study:

  • To propose a novel and efficient algorithm for faster topic detection on social media.
  • To enhance the accuracy and speed of topic detection without compromising performance.
  • To introduce the Node Significance based Label Propagation Community Detection (NSLPCD) algorithm.

Main Methods:

  • Keyword frequency distribution analysis to identify topic-identifying and topic-describing keywords.
  • Construction of a keyword co-occurrence graph based on identified keywords.
  • Application of the Node Significance based Label Propagation Community Detection (NSLPCD) algorithm for topic clustering.

Main Results:

  • The NSLPCD algorithm effectively detects topics faster than existing methods.
  • The proposed approach maintains accuracy while improving detection speed.
  • Experimental results on Twitter data demonstrate superior quality and run-time performance.

Conclusions:

  • The NSLPCD algorithm offers an effective solution for rapid and accurate topic detection in large-scale social media data.
  • This method addresses the limitations of existing approaches in real-time information analysis.
  • The algorithm's performance validates its utility for social media analytics and information monitoring.