整个不信任生态系统的流行后弹性增加
Lucia Illari1,2, Nicholas J Restrepo3, Neil F Johnson4,5
1Dynamic Online Networks Laboratory, George Washington University, Washington, DC, 20052, USA.
Scientific reports
|September 20, 2023
概括
由于相互关联的主题和规模,在线不信任医疗专业知识越来越大. 有效的缓解需要"全球性"信息传递,将疾病信息与气候变化结合起来,并将地方与国家重点放在一起.
科学领域:
- 社会科学 社会科学 社会科学
- 公共卫生 公共卫生
- 计算社会科学 计算社会科学
背景情况:
- 在线不信任医疗专业知识是越来越令人担忧的,尽管正在进行缓解努力.
- 大型Facebook网络中的大流行前话语集中在疫苗 (不信任) 上.
- 流行后的话语扩展到相互关联的非疫苗主题和不同的地理尺度.
研究的目的:
- 分析有关信任和不信任的在线话语的演变.
- 了解相互连接如何使系统层面的阻力对缓解策略产生.
- 为应对网络不信任提出新的,可扩展的解决方案.
主要方法:
- 分析了大约1亿用户的Facebook生态系统中的话语.
- 检查大流行前和后的沟通模式.
- 数字模拟用于建模网络动态和测试缓解策略.
主要成果:
- 疫情后的言论相互关联了多个主题和地理尺度,创造了系统层面的阻力.
- 针对孤立主题或规模的缓解努力由于这种相互联系,效果较差.
- 系统层面的分析揭示了导致在线不信任的复杂动态.
结论:
- 需要反直觉的解决方案来有效和可扩展地缓解在线不信任.
- 通过战略性地结合主题 (例如气候变化的疾病) 和地理尺度 (地方和国家) 来提出"全球"信息.
- 这种混合方法为解决复杂的在线信任动态提供了更强大的战略.
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