政治立场检测中的社会背景:影响和推断
Ramon Villa-Cox1,2, Evan M Williams2, Kathleen M Carley2
1ESPAE, Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador.
PloS one
|June 26, 2025
概括
利用社交网络显著改善了政治立场检测模型. 分析用户交互可以提高模型的性能,并将其推断到新国家和未来的事件.
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.
- 机器学习 机器学习
背景情况:
- 立场检测对于分析论和识别有害内容,如错误信息和仇恨言论至关重要.
- 现有的模型往往难以将其推广到新的背景或未来事件中.
- 像Twitter这样的社交媒体平台提供了丰富的数据来了解用户的立场和互动.
研究的目的:
- 评估政治立场检测模型的性能和推断能力.
- 研究将用户社交网络 (自我网络) 纳入姿势检测中的影响.
- 评估模型在不同国家和时间段的通用性.
主要方法:
- 利用来自2019年南美抗议活动的大规模,标签不清的Twitter数据集.
- 开发了基于变压器的编码器,用于用户和推特嵌入.
- 采用异质图表注意力网络 (GAT) 来进行立场预测.
- 分析了模型在预测不同国家背景和不同国家背景内的用户立场方面的表现.
主要成果:
- 整合用户的自我网络持续改善了国内政治立场检测性能.
- 利用社会背景显著提高了模型将立场预测推断到新国家背景的能力.
- 当使用社会背景时,这些模型显示了对未来事件的更好的概括性.
结论:
- 社交网络信息是提高政治立场检测模型的准确性和普遍性的一个有价值的功能.
- 基于图形的方法,结合社会背景,为强大的立场检测提供了一个有希望的方向.
- 这些发现对改善民意调查,发现错误信息和理解在线政治话语有影响.
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