利用深度学习来检测西班牙语推特中关于COVID-19疫苗接种的立场
Guillermo Blanco1,2,3, Rubén Yáñez Martínez1, Anália Lourenço1,2,3,4
1ESEI, Department of Computer Science, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense 32004, Spain.
JAMIA open
|February 26, 2025
概括
这项研究开发了针对COVID-19疫苗接种推特的西班牙立场检测模型,发现BERT-Multi+BiLSTM对虚假信息检测等公共卫生应用有效.
科学领域:
- 计算语言学 计算语言学
- 公共卫生信息学 公共卫生信息学
- 社交媒体分析 社交媒体分析
背景情况:
- 自动姿势检测对于公共卫生至关重要,特别是在健康危机期间.
- 现有的模型主要集中在英语上,限制了全球适用性.
- 将姿势检测扩展到西班牙语等其他语言是必不可少的.
研究的目的:
- 开发和评估西班牙语社交媒体帖子的立场检测模型.
- 为了比较西班牙语推文姿态检测的传统和深度学习模型.
- 评估多语言和特定语言嵌入的有效性.
主要方法:
- 创建了一个手动注释的6170条西班牙推特关于COVID-19疫苗接种的语料库.
- 进行了传统模型 (TF-IDF+SVM) 和深度学习模型 (BERT-Multi+BiLSTM,BETO+BiLSTM,RoBERTa BNE-LSTM) 的比较.
- 使用F1,马修斯相关系数和接收器操作曲线 (AUC) 下的面积来评估性能.
主要成果:
- 在BERT-Multi+BiLSTM模型中,实现了最高的性能 (宏F1:0.86,MCC:0.79,AUC:0.95,0.85,0.97).
- 多语言嵌入对于这个主题来说表现优于特定语言的嵌入.
- 该模型表现出强大的预测能力,特别是针对反对疫苗接种的推文.
结论:
- 开发的BERT-Multi+BiLSTM模型适用于公共卫生应用,包括宣传活动和错误信息检测.
- 该研究为未来的研究提供了有价值的资源 (数据集和代码).
- 在西班牙社交媒体上有效的立场检测可以帮助公共卫生干预.
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