虚假信息的数学建模和缓解政策的有效性
David J Butts1, Sam A Bollman2, Michael S Murillo3
1Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, 48824, USA. buttsdav@msu.edu.
Scientific reports
|November 1, 2023
概括
数学建模优化了对抗虚假信息的战略. 删除代理人或增加公众的怀疑主义被证明是有效的,而反击活动需要广泛的影响力来有效打击假新闻.
科学领域:
- 计算社会科学 计算社会科学
- 网络科学 网络科学
- 数学建模的数学建模
背景情况:
- 虚假信息运动的目的是操纵论.
- 需要有效的策略来打击虚假信息的传播.
研究的目的:
- 检查和优化打击虚假信息的战略.
- 调查内容调节,教育和反击活动对信息传播动态的影响.
主要方法:
- 利用了一个修改后的二进制协议模型,使用加权,定向和异质网络.
- 纳入真实社交网络数据进行分析.
- 在不同的干预策略下研究了临界点属性.
主要成果:
- 通过删除随机或有影响力的代理来调节内容显示了可比的有效性.
- 增加公众怀疑的教育策略比准有偏见的代理更有效.
- 成功的反击活动需要大量的人口影响力来反对虚假信息.
结论:
- 从任何网络位置删除虚假信息都可能是有效的.
- 培养怀疑的公共教育是对抗虚假信息的有力工具.
- 具有广泛影响力的战略反击活动对于打击虚假信息至关重要.
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