在多平台社交媒体宇宙中非线性传播行为
1Physics Department, George Washington University, Washington DC 20052, USA.
Chaos (Woodbury, N.Y.)
|April 22, 2024
概括
当社区在平台上频繁链接时,即使感染率低,有害内容也在网上广泛传播. 减少链路丢失是防止错误信息和仇恨言论广泛爆发的关键.
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:
- 在线社区在多个社交媒体平台上动态地相互连接.
- 了解有害内容 (错误信息,仇恨言论) 的病毒传播是一个关键的社会挑战.
- 现有的模型往往不能完全捕捉跨平台社区动态.
研究的目的:
- 开发一种非线性动态模型,用于在互联网社区中病毒传播有害内容.
- 为了确定全系统疫情爆发的分析条件.
- 为政策干预提供信息,防止有害在线内容的传播.
主要方法:
- 开发一个非线性动态模型.
- 平均场理论的应用 (有效介质理论).
- 与详细的数值模拟进行比较.
主要成果:
- 该模型准确地预测了病毒传播动态.
- 为爆发发病的分析条件被推导出来.
- 当社区间连接率高,链路丢失率低时,无论感染率与恢复率,预计会有系统范围的传播.
结论:
- 社区间链接的高率和链接丢失的低率可以导致广泛的有害内容爆发.
- 有效的媒介理论为分析跨平台内容动态提供了一个强大的框架.
- 决策者必须在设计策略时考虑多社区动态,以打击在线错误信息和仇恨言论.
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