Using Gaussian Mixtures to Model Evolving Multi-Modal Beliefs Across Social Media
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909763582820352 |
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| author | Chen, Yijun Farokhi, Farhad Bu, Yutong Low, Nicholas Kah Yean Horstman, Jarra Greentree, Julian Evans, Robin Melatos, Andrew |
| author_facet | Chen, Yijun Farokhi, Farhad Bu, Yutong Low, Nicholas Kah Yean Horstman, Jarra Greentree, Julian Evans, Robin Melatos, Andrew |
| contents | We use Gaussian mixtures to model formation and evolution of multi-modal beliefs and opinion uncertainty across social networks. In this model, opinions evolve by Bayesian belief update when incorporating exogenous factors (signals from outside sources, e.g., news articles) and by non-Bayesian mixing dynamics when incorporating endogenous factors (interactions across social media). The modeling enables capturing the richness of behavior observed in multi-modal opinion dynamics while maintaining interpretability and simplicity of scalar models. We present preliminary results on opinion formation and uncertainty to investigate the effect of stubborn individuals (as social influencers). This leads to a notion of centrality based on the ease with which an individual can disrupt the flow of information across the social network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using Gaussian Mixtures to Model Evolving Multi-Modal Beliefs Across Social Media Chen, Yijun Farokhi, Farhad Bu, Yutong Low, Nicholas Kah Yean Horstman, Jarra Greentree, Julian Evans, Robin Melatos, Andrew Systems and Control Social and Information Networks We use Gaussian mixtures to model formation and evolution of multi-modal beliefs and opinion uncertainty across social networks. In this model, opinions evolve by Bayesian belief update when incorporating exogenous factors (signals from outside sources, e.g., news articles) and by non-Bayesian mixing dynamics when incorporating endogenous factors (interactions across social media). The modeling enables capturing the richness of behavior observed in multi-modal opinion dynamics while maintaining interpretability and simplicity of scalar models. We present preliminary results on opinion formation and uncertainty to investigate the effect of stubborn individuals (as social influencers). This leads to a notion of centrality based on the ease with which an individual can disrupt the flow of information across the social network. |
| title | Using Gaussian Mixtures to Model Evolving Multi-Modal Beliefs Across Social Media |
| topic | Systems and Control Social and Information Networks |
| url | https://arxiv.org/abs/2509.01123 |