量化争议:检测错误信息的新方法
Oliver Li1, Sahiti Myneni2, Trevor Cohen1
1University of Washington.
Studies in health technology and informatics
|August 8, 2025
概括
社交媒体的错误信息会损害公众健康. 这项研究引入了一种新的争议测量方法,以了解用户的反应,有助于开发针对健康错误信息的干预措施.
科学领域:
- 公共卫生 公共卫生
- 社交媒体分析 社交媒体分析
- 计算语言学 计算语言学
背景情况:
- 社交媒体上的错误信息给公共卫生倡议带来了重大挑战.
- 了解用户接收和对错误信息的反应对于有效的干预设计至关重要.
研究的目的:
- 开发一种用于衡量社交媒体帖子争议性的新型指标.
- 调查错误信息和争议后的关系.
- 为制定针对错误信息的公共卫生干预措施提供信息.
主要方法:
- 利用Llama 3.1的上下文学习用于社交媒体帖子上的立场检测,在COVID-19数据集上实现高精度.
- 开发了一种基于预测用户对帖子的反应态度的度的争议度量.
- 评估了包含错误信息的帖子具有更高争议性的假设.
主要成果:
- 使用Llama 3.1.实现了85.3%和71.8%的准确性在COVID-19立场检测中.
- 展示了一种基于立场变化的方法来量化社交媒体后的争议.
- 提供经验证据支持错误信息与增加后争议之间的联系.
结论:
- 开发的争议度量为分析社交媒体信息接收提供了一个新的镜头.
- 调查结果表明,有争议的帖子可能是存在错误信息的关键指标.
- 这项研究对设计有针对性的公共卫生战略来打击在线虚假信息有影响.
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