低和平国家和高和平国家的新闻媒体中的词语差异是由自然语言处理和机器学习揭示的
Larry S Liebovitch1,2, William Powers1, Lin Shi1
1Department of Physics, Queens College, City University of New York, New York, New York, United States of America.
PloS one
|November 1, 2023
概括
这项研究使用机器学习确定了和平和冲突的语言标记. 它揭示了"和平言论"和"仇恨言论"如何反映和影响社会和平水平,创造了一个新的定量和平指数.
科学领域:
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
- 社会语言学 社会语言学
背景情况:
- 语言在社会过程中起着双重作用,影响冲突和和平.
- 仇恨言论被认为具有煽动暴力和破坏的潜力.
- 了解的特点的特点.
- 和平的演讲 和平的演讲
- 这对于维持社会和至关重要.
研究的目的:
- 确定语言特征,区分较低和平和较高和平的国家.
- 探索语言使用与社会和平水平之间的关系.
- 利用自然语言处理 (NLP) 和机器学习 (ML) 开发数据驱动的和平量化指标.
主要方法:
- 利用现有的和平指数和在线新闻媒体数据.
- 采用机器学习和自然语言处理来分析单词频率.
- 训练了一个来自极低和平和极高和平国家的单词频率模型,以分类中等和平国家.
主要成果:
- 确定了与较低和平与较高和平国家密切相关的特定词语.
- 开发的ML模型根据语言模式准确地分类国家.
- 成功生成了中等和平国家的定量和平指数,该指数位于已建立的低和平和高和平基准之间.
结论:
- 自然语言处理和机器学习可以产生新的定量社会措施.
- 语言差异可以作为一个国家的和平程度的可靠指标.
- 这种方法提供了一种通过语言分析评估和理解社会和平的新方法.
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