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This study models online opinion evolution using social influence. A consensus model accurately predicts collective behavior, with errors mainly from unpredictable human judgment, not model flaws.

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Area of Science:

  • Social Psychology
  • Computational Social Science
  • Collective Behavior Analysis

Background:

  • Social influence significantly shapes opinion evolution and judgment revision.
  • Existing models often lack validation against independent, real-world datasets.
  • Understanding prediction limitations in opinion dynamics is crucial for social science.

Purpose of the Study:

  • To quantitatively model and predict opinion evolution in online collective behavior.
  • To validate a consensus model using a large-scale crowdsourced experiment with independent data.
  • To identify and measure the sources of prediction error in opinion dynamics.

Main Methods:

  • Conducted a crowdsourced in vitro experiment with 861 participants.
  • Applied a consensus model to predict opinion dynamics based on individual influenceability.
  • Validated the model on data not used for calibration to prevent overfitting.
  • Developed a novel measure of unpredictability in human judgment revision.

Main Results:

  • The consensus model demonstrated significant predictive power for opinion evolution.
  • Prediction accuracy was enhanced by prior knowledge of participant behavior.
  • Unpredictable variations in human judgment accounted for over two-thirds of prediction errors.
  • Model imperfections were a minor source of error compared to inherent unpredictability.

Conclusions:

  • Quantitative models can effectively predict online collective behavior and opinion dynamics.
  • Human judgment revision contains inherent unpredictability that limits predictive accuracy.
  • Future research should focus on modeling the sources of this unpredictability.