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Modeling information diffusion in online social networks using a modified forest-fire model.

Sanjay Kumar1,2, Muskan Saini3, Muskan Goel4

  • 1Department of Computer Science and Engineering, Delhi Technological University, Main Bawana Road, New Delhi, 110042 India.

Journal of Intelligent Information Systems
|October 19, 2020
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Summary
This summary is machine-generated.

This study models information diffusion on social networks using a modified forest-fire approach. The novel model effectively predicts how information spreads by incorporating user behavior and network structures.

Keywords:
Forest-fire modelInformation diffusionNature-inspired algorithmOnline social networksTwitter

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Area of Science:

  • Network Analysis
  • Computational Social Science
  • Information Science

Background:

  • The rapid evolution of social media has increased the importance of understanding information dissemination.
  • Modeling information diffusion is crucial for network analysis and understanding online behavior.

Purpose of the Study:

  • To develop a novel model for information propagation in online social networks.
  • To enhance existing models by incorporating user behavior and network dynamics.

Main Methods:

  • A modified forest-fire model is proposed, adapting concepts of fire spread to information diffusion.
  • Users are categorized as 'Empty', 'Tree', or 'Fire', with a new 'Burnt' state introduced for non-spreaders.
  • The model was validated using six real-world datasets from Twitter.

Main Results:

  • The modified forest-fire model demonstrates effectiveness in predicting information diffusion patterns.
  • The inclusion of the 'Burnt' state improves the model's accuracy in representing network dynamics.
  • The model shows robust performance across diverse real-world social network data.

Conclusions:

  • The proposed nature-inspired model offers a valuable tool for analyzing information spread on social media.
  • This research contributes to the field of network analysis by providing an improved method for modeling diffusion.
  • The findings have implications for understanding and managing information flow in online environments.