选民模型可以准确地预测在线群体中的个人意见
1CY Cergy Paris University, Complex Systems Institute of Paris Île-de-France (ISC-PIF) CNRS, médialab, Sciences Po, 75007 Paris, France; , 75013 Paris, France; and Learning Planet Institute, Learning Transitions unit, Paris, France.
Physical review. E
|August 1, 2025
概括
多州选民模型准确地预测了Twitter上的个人政治观点,验证了其在复杂的社会动态和用户层面分析中的使用.
科学领域:
- 计算社会科学 计算社会科学
- 意见动态建模 意见动态建模
背景情况:
- 意见动态模型经常复制宏观层面的趋势,但缺乏用户层面的验证.
- 这些模型在细粒度的个人数据上进行现实世界的测试是有限的.
研究的目的:
- 评估多州选民模型捕获个人意见的能力.
- 为了评估模型的表现,使用在Twitter上使用2017年法国总统大选的真实世界数据集.
主要方法:
- 利用了多州选民模式与狂热分子.
- 分析了2017年法国总统大选期间微细分析的Twitter用户意见数据集.
- 相关的模型生成的意见分布与基本真相政治倾向.
主要成果:
- 在模型的平衡意见分布和用户实际的政治倾向之间显示出强烈的对应.
- 展示了分歧概率有效地识别志同道合的用户对.
- 验证了选民模型在复杂,现实世界的社交网络环境中的适用性.
结论:
- 多州选民模型在预测社交网络中的个人意见方面表现出显著的有效性.
- 强调了用户级经验评估对于意见动态模型的重要性.
- 建议进一步研究用现实数据验证计算社会科学模型.
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