一个单一的模型足够吗? 系统地比较计算方法来检测民粹主义的极端右翼内容
Mykola Makhortykh1, Ernesto de León1, Clara Christner2
1Institute of Communication and Media Studies, University of Bern, Fabrikstrasse 8, 3012 Bern, Switzerland.
Quality & quantity
|May 9, 2025
概括
这项研究比较了在线检测民粹主义激进右翼 (PRR) 内容的计算方法. 采用密集预处理的深度学习模型表现最好,但检测PRR仍然具有挑战性,特别是在杂的数据中.
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.
- 政治沟通是政治沟通.
背景情况:
- 人民主义极端右翼 (PRR) 思想日益流行,需要了解在线参与PRR内容.
- 通过计算检测PRR内容是复杂的,因为不同的在线道和数据特征.
研究的目的:
- 系统地比较各种计算方法来检测文本数据中的PRR内容.
- 评估文本预处理技术对模型性能的影响.
- 评估集合模型在增强PRR检测方面的有效性.
主要方法:
- 使用了66个词典,监督机器学习和深度学习 (DL) 模型.
- 测试了三个德语语系数据集的模型,噪音水平各不相同.
- 检查了330个组合模型,结合了不同的方法.
主要成果:
- 深度学习模型,特别是具有计算密集型预处理的模型,产生了最佳的个体模型性能.
- 在噪音较高的数据集上,性能仍然低于最佳.
- 集合模型显示了边际改进,通常与单个DL模型相比性能相提升.
结论:
- 对PRR内容的计算检测是一项具有挑战性的任务.
- DL模型提供了最有前途的个体方法,但数据质量显著影响有效性.
- 需要进一步的研究来克服噪音或复杂的文本环境中的局限性.
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