新闻媒体的自动大规模政治偏见检测
Ronja Rönnback1, Chris Emmery1, Henry Brighton1
1Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands.
PloS one
|May 12, 2025
概括
研究人员开发了一种机器学习方法来量化在线新闻中的政治偏见. 这种可扩展的方法分析了多种偏见形式,准确地估计了媒体的倾向,并解释了大量新闻领域的分类.
科学领域:
- 计算社会科学 计算社会科学
- 媒体研究 媒体研究
- 自然语言处理自然语言处理.
背景情况:
- 政治偏见在新闻媒体中很普遍,但量化它是具有挑战性的.
- 现有的由人类标记的数据库成本高昂,范围有限.
- 以前的研究往往忽略了除了有偏见的措辞之外的各种偏见表达.
研究的目的:
- 引入数据驱动的机器学习方法,用于分析在线新闻中的政治偏见.
- 克服先前方法在偏差分析的规模和范围上的局限性.
- 为新闻媒体提供政治倾向和解释的准确估计.
主要方法:
- 利用机器学习技术进行数据驱动的在线新闻分析.
- 开发了一种方法来分析多种形式的政治偏见,包括报告遗漏.
- 应用了这种方法来估计数十万个Web域的政治倾向.
主要成果:
- 在估计众多网络域名的政治倾向方面取得了高准确性.
- 启用了对新闻媒体分配的政治偏见的详细解释.
- 为全面的政治偏见研究展示了一个可扩展的解决方案.
结论:
- 拟议的机器学习方法为研究政治偏见提供了一个可扩展和全面的方法.
- 这种数据驱动技术克服了传统方法的成本和范围限制.
- 提供了对在线新闻报道中政治偏见的多面性质的更深入的见解.
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