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在社交媒体平台上大规模测量对错误信息的敏感性
Christopher K Tokita1, Kevin Aslett2,3, William P Godel2
1Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.
PNAS nexus
|October 9, 2024
概括
测量在线虚假信息的情况.
科学领域:
- 社会科学 社会科学 社会科学
- 计算社会科学 计算社会科学
- 信息科学 信息科学 信息科学
背景情况:
- 对于在线虚假信息影响的传统指标 (例如,浏览量,分享量) 是不够的.
- 这些指标不考虑用户对相信错误信息的不同易受性.
研究的目的:
- 开发和验证一种用于估计在线虚假信息影响的新方法.
- 确定用户最有可能相信错误信息的特征,并评估干预的有效性.
主要方法:
- 将调查数据与观察性推特数据结合起来,以估计暴露于错误信息和可能相信错误信息的用户.
- 将该方法应用于139篇病毒性新闻文章,并模拟了社交媒体干预.
主要成果:
- 容易相信虚假新闻的用户往往持有更极端的意识形态,更早地遇到错误信息.
- 模拟干预措施在减少敏感用户暴露方面表现出适度的有效性.
- 如果不在错误信息传播后不久实施干预,干预的有效性会迅速下降.
结论:
- 仅依赖暴露或相互作用数据低估了错误信息的真正影响.
- 更精确的影响估计需要关注那些可能相信错误信息的用户.
- 早期和有针对性的干预对于有效的缓解策略至关重要.
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