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A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
Published on: November 21, 2019
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Emergent user behavior on Twitter modelled by a stochastic differential equation.
Anders Mollgaard1, Joachim Mathiesen1
1Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.
Plos One
|May 9, 2015
Summary
This study models collective human dynamics using Twitter data, revealing that aggregated user interest in brands creates bursty tweet rates and 1/f noise, accurately reproduced by a stochastic differential equation.
Area of Science:
- Computational Social Science
- Statistical Physics
- Network Science
Background:
- Social media platforms like Twitter generate vast amounts of data reflecting collective human behavior.
- Analysis of tweet rates reveals bursty dynamics and characteristic 1/f noise, indicative of complex user interactions.
- Understanding these dynamics is crucial for modeling crowd behavior and information diffusion.
Purpose of the Study:
- To model the collective human dynamics underlying fluctuations in brand name tweet rates on Twitter.
- To investigate the relationship between aggregated user interest and observed bursty dynamics.
- To develop and validate a stochastic model that captures the statistical properties of tweet rate fluctuations.
Main Methods:
- Utilized Twitter data to track fluctuations in brand name tweet rates.
- Defined and analyzed aggregated "user interest" as a proxy for collective behavior.
- Developed a stochastic differential equation with multiplicative noise to model the observed dynamics.
- Performed detailed analysis of tweet rate fluctuations to validate the model's predictions.
Main Results:
- Identified strongly correlated user behavior as the driver of bursty collective dynamics.
- Demonstrated that aggregated user interest can be modeled using a stochastic differential equation.
- The proposed model accurately reproduces the observed bursty dynamics and the characteristic 1/f noise in tweet rates.
- Model validation confirmed its ability to capture the essential features of real-world social media data.
Conclusions:
- Collective human dynamics on social media exhibit predictable patterns, including burstiness and 1/f noise.
- A stochastic differential equation with multiplicative noise effectively models user interest and its impact on tweet rates.
- This research provides a quantitative framework for understanding and predicting collective behavior in online environments.
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