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A Multi-Information Spreading Model for One-Time Retweet Information in Complex Networks.

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Summary

We developed a Susceptible-Infected-Completed (SIC) model to understand how multiple information pieces spread on social networks. Competing information hinders spread, while cooperating information enhances it, depending on interaction factors.

Keywords:
enhancement factorinhibiting factormulti-information spreadingone-time retweet informationonline social network

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Area of Science:

  • Social Network Analysis
  • Information Spreading Dynamics
  • Computational Social Science

Background:

  • Information diffusion in online social networks is complex and influenced by various factors.
  • Understanding how multiple information pieces interact is crucial for network analysis.

Purpose of the Study:

  • To propose and analyze a Susceptible-Infected-Completed (SIC) multi-information spreading model.
  • To investigate the dynamics of one-time retweet information spreading under different interaction scenarios.

Main Methods:

  • Developed a Susceptible-Infected-Completed (SIC) multi-information spreading model.
  • Introduced inhibiting and enhancement factors to simulate information interactions.
  • Conducted experiments on BA scale-free networks and the Twitter network.

Main Results:

  • Competing information negatively impacts the spread of other information.
  • Cooperating information amplifies the spread of mutually beneficial content.
  • The strength of enhancement factors dictates spread dynamics in mixed scenarios.

Conclusions:

  • The SIC model provides insights into multi-information propagation patterns.
  • Interaction dynamics (competition vs. enhancement) significantly alter information spread.
  • Network analysis can benefit from understanding these complex information interactions.