转移学习驱动的假新闻检测和分类使用大型语言模型
Basma S Alqadi1, Suliman A Alsuhibany2, Samia Nawaz Yousafzai3
1Computer Science Department,College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.
Scientific reports
|August 5, 2025
概括
这项研究引入了一种新的转移学习框架,用于检测假新闻,特别是有限的数据. 我们的方法比现有模型提高了至少3.9%的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算社会科学 计算社会科学
背景情况:
- 社交媒体上虚假新闻的快速传播给社会和个人带来了重大挑战.
- 目前的假新闻检测方法在有限的数据,语言复杂性和嵌入式集成方面扎.
研究的目的:
- 为有限的数据场景开发一个强大的假新闻检测框架.
- 提高虚假新闻分类模型的准确性和可解释性.
主要方法:
- 提出了一个多阶段的转移学习框架,使用RoBERTa,一个预训练的大型语言模型.
- 系统地比较了文字嵌入技术 (Word2Vec,一热编码) 与精致的微调.
- 在两个真实世界的基准数据集上评估了框架.
主要成果:
- 与最先进的模型相比,实现了3.9%的最低精度改进.
- 在有限的数据设置中证明了转移学习方法的有效性.
- 提供了关于嵌入技术对虚假新闻分类的影响的见解.
结论:
- 新的多阶段转移学习框架为虚假新闻检测提供了更强大,更准确的解决方案.
- 该方法有效地解决了现有方法的局限性,特别是在低数据环境中.
- 这项工作强调了嵌入技术和转移学习对于可靠的假新闻识别的重要性.
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