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Sentiment Contagion Based on the Modified SOSa-SPSa Model.

Zhijie Song1, Rui Shi1, Jie Jia1

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This study introduces an extended model for sentiment contagion, incorporating interactions between optimists and pessimists. Findings reveal that pessimist recovery speed significantly influences contagion dynamics and the spread of pessimism.

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Area of Science:

  • Social dynamics
  • Computational social science
  • Epidemiological modeling

Background:

  • Sentiment contagion, akin to infectious diseases, describes how emotions spread within populations.
  • Existing models like SOSa-SPSa (susceptible-optimistic-susceptible and susceptible-pessimistic-susceptible) provide a framework for understanding this phenomenon.
  • However, these models often simplify the complex interactions between different sentiment groups.

Purpose of the Study:

  • To extend the SOSa-SPSa model by incorporating direct interactions between optimistic and pessimistic individuals.
  • To analyze how these interactions affect the overall sentiment contagion process.
  • To investigate the influence of key parameters, such as recovery speed and spontaneous generation, on the prevalence of pessimism.

Main Methods:

  • Utilized agent-based simulation to model sentiment contagion dynamics.
  • Extended the SOSa-SPSa model to include three distinct population groups: susceptible, optimistic, and pessimistic individuals.
  • Analyzed simulation results to quantify the spread of sentiments and the impact of parameter variations.

Main Results:

  • The extended model provides a more comprehensive explanation of sentiment contagion by accounting for inter-group dynamics.
  • The recovery speed of pessimists significantly regulates the number of pessimists and the likelihood of optimists adopting pessimism.
  • Pessimism demonstrates a stronger influence than reversion to susceptibility, impacting contagion outcomes.
  • The number of pessimists positively correlates with optimists becoming pessimistic but negatively with pessimists reverting to optimism.
  • A slow spontaneous generation rate leads to a sharp increase in pessimists, while a higher rate shows diminishing returns.

Conclusions:

  • The extended SOSa-SPSa model offers a more nuanced understanding of sentiment contagion by considering optimist-pessimist interactions.
  • Pessimist recovery speed is a critical factor in controlling the spread and prevalence of negative sentiment.
  • The model highlights the complex interplay between individual sentiment states and population-level contagion dynamics.