离散选择模型中的道德修辞:一种自然语言处理方法
Teodóra Szép1, Sander van Cranenburgh1, Caspar Chorus1,2
1Engineering Systems and Services, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, The Netherlands.
Quality & quantity
|June 26, 2023
概括
这项研究引入了一种新的方法,将选择和文本数据结合起来,以了解道德动机. 使用自然语言处理获得的道德修辞,在政治学研究中显著解释了投票行为.
科学领域:
- 社会心理学 社会心理学
- 计算社会科学 计算社会科学
- 政治科学 政治科学是指政治学.
背景情况:
- 了解人类行为背后的道德动机是复杂的.
- 现有的方法通常会单独分析行为或声明偏好.
- 将语言表达方式与行为数据相结合,提供了更丰富的视角.
研究的目的:
- 开发和验证一种用于推断道德动机的新方法.
- 将选择数据与文本数据结合起来,以增强行为分析.
- 探索道德修辞在政治决策中的作用.
主要方法:
- 使用自然语言处理 (NLP) 来从文本中提取道德价值 (道德修辞).
- 使用道德基础理论作为道德价值识别的框架.
- 将提取的道德修辞作为输入集成到离散选择模型中.
- 在欧洲议会投票和党派叛逃的案例研究分析.
主要成果:
- 道德修辞在模拟投票行为方面表现出显著的解释力.
- 提出的方法成功地将文本道德价值观与选择数据相结合.
- 对道德行为的洞察力是通过分析言语和行为来获得的.
结论:
- 这种新的方法提供了一种强有力的方法来推断道德动机.
- 道德修辞是政治行为的有价值的预测指标.
- 未来的研究可以将这种方法扩展到人类活动的其他领域.
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