寻找黄金:对跨平台方法进行比较分析,用于在文本数据中自动检测政治内容
Mykola Makhortykh1, Ernesto de León1, Aleksandra Urman2
1Institute of Communication and Media Studies, University of Bern, Bern, Switzerland.
PloS one
|November 18, 2024
概括
需要新的方法来检测在线政治内容. 深度学习和机器学习模型在清洁数据上表现最好,而基于字典的模型在杂的在线政治信息中更强大.
科学领域:
- 计算社会科学 计算社会科学
- 数字人文学科 数字人文学科
- 政治沟通的政治沟通
背景情况:
- 了解政治信息消费在当今高选择性媒体环境中至关重要.
- 需要新的方法来跟踪用户与在线内容的互动,并识别政治信息.
- 自动化内容分析为分析大量数字数据提供了一个潜在的解决方案.
研究的目的:
- 对自动化内容分析技术用于检测德国政治内容的性能进行比较分析.
- 评估基于字典的经典监督机器学习和深度学习方法.
- 评估数据预处理对这些检测方法有效性的影响.
主要方法:
- 对三组自动化内容分析技术的比较分析:基于字典的经典监督机器学习和深度学习.
- 使用三个不同的数据集进行验证.
- 检查了66个具有不同数据预处理模式的模型.
主要成果:
- 深度学习和经典机器学习模型在较低噪音的数据集上实现了最佳性能.
- 基于字典的模型在处理杂数据时表现出更强大的性能.
- 发现数据预处理对模型性能的影响有限.
结论:
- 自动化内容分析技术显示出在线检测政治信息的前景.
- 技术的选择 (深度学习,机器学习或基于字典的) 取决于数据的性质 (噪音与清洁).
- 进一步的研究可以完善这些方法,以便在数字环境中更准确地检测政治内容.
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