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This study introduces a new model for evaluating topic influence in social networks, addressing inaccuracies in current methods. The model quantifies topic influence using five key indicators for better hot topic analysis.

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Area of Science:

  • Social Network Analysis
  • Information Science

Background:

  • Social networks are crucial in industrial society, with research focusing on topic detection and trend prediction.
  • Topic influence evaluation in social networks is underdeveloped, leading to inaccurate calculations.

Purpose of the Study:

  • To propose a novel model for evaluating the real-time relative influence of topics in social networks.
  • To address the problem of inaccurate influence calculation in hot topic research.

Main Methods:

  • Defined five impact indicators: user engagement, topic coverage, topic activity, topic persistence, and topic novelty.
  • Incorporated relative influence and time attenuation characteristics, beyond traditional metrics like likes, forwards, and comments.

Main Results:

  • The proposed model quantifies topic influence effectively.
  • Identified influential factors contributing to topic hotness.
  • Experimental results demonstrate rapid aggregation of influence factors and accurate influence indication.

Conclusions:

  • The novel model accurately evaluates real-time relative topic influence in social networks.
  • The model provides a more refined understanding of topic hotness by considering multiple factors.