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利用GloVe嵌入的可解释的新组合与深度CNN-BiLSTM神经网络进行假新闻检测

Sidra Khalid¹, Shabana Ramzan¹, Muhammad Munwar Iqbal²

¹Department of Computer Science and IT, Government Sadiq College Women University, Bahawalpur, Pakistan.

|September 22, 2025

概括

这项研究引入了用于假新闻检测的混合深度学习模型,达到99.63%的准确性. 整合可解释的人工智能 (XAI) 确保了模型透明度,并建立了用户对检测错误信息的信任.

科学领域:

人工智能的人工智能
自然语言处理自然语言处理.
计算社会科学计算社会科学

背景情况:

假新闻显著影响着社会决策和公众的看法.
开发可靠的假新闻检测系统对于保持知情的公共话语至关重要.
现有的方法往往缺乏透明度,阻碍了用户的信任和实际实施.

研究的目的:

对创新的混合深度学习模型和嵌入技术进行比较分析,用于检测假新闻.
通过使用可解释的人工智能 (XAI) 提高虚假新闻检测模型的可解释性.
为了评估模型的性能和可靠性,用于实际应用.

主要方法:

混合深度学习模型的比较分析,包括GloVe与CNN-BiLSTM,FastText-Bi-LSTM以及TF-IDF的后勤回归.
与CNN和GloVe等基线模型进行测试,使用BiLSTM和CNN.
使用局部可解释的模型不可知解释 (LIME) 来实现模型可解释性.

主要成果:

采用BiLSTM和CNN模型的GloVe实现了99.63%的高精度.
没有FastText的LSTM表现为98.33%.
LIME成功地澄清了特征的重要性,解释了高模型精度.

结论:

将先进的深度学习模型与XAI集成,在假新闻检测中提供了高准确性和可解释性.
这种方法解决了模型性能和用户信任的关键方面.
该研究为实施强大可靠的假新闻检测系统奠定了基础.

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