不对称的意识形态隔离在Facebook上的政治新闻
Sandra González-Bailón1, David Lazer2, Pablo Barberá3
1Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA.
概括
在Facebook上
科学领域:
- 社会科学
- 计算机科学
- 政治科学
背景情况:
- 像Facebook这样的社交媒体平台上的算法策划可以影响政治新闻的消费.
- 了解意识形态分离对于评估在线民主话语的健康至关重要.
研究的目的:
- 调查2020年美国大选期间Facebook上政治新闻消费的意识形态分离程度.
- 分析算法策划和用户参与如何促进这种分离.
- 检查在意识形态上分离的网络社区中存在的错误信息.
主要方法:
- 分析了2020年美国大选期间的Facebook用户数据 (208万美国用户).
- 对潜在的新闻曝光,实际曝光和用户与政治新闻的互动进行比较.
- 使用Meta的第三方事实检查程序数据识别错误信息.
主要成果:
- 观察到高度的意识形态分离,从潜在的接触到参与.
- 不对称的隔离, 有一个独特的, 独家保守的新闻角落.
- 大部分被发现的错误信息集中在保守派主导的新闻圈.
结论:
- 脸书的平台设计和算法策划有助于政治新闻的意识形态分离.
- 这些发现突显了隔离的不对称性,
- 这些模式引发了对错误信息的传播和政治话语的分裂的担忧.
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