使用大字嵌入混合和定制的CNN模型预测假新闻立场检测的新方法
1Department College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia.
PloS one
|December 13, 2024
概括
这项研究引入了一种新的框架,用于使用组合词嵌入和卷积神经网络 (CNN) 来自动检测假新闻. 该方法实现了高精度,为打击错误信息提供了强有力的解决方案.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 错误信息的快速传播带来了重大的社会和个人风险.
- 需要自动化系统来早期检测假新闻的需求至关重要.
- 现有的方法很难跟上在线信息传播的速度.
研究的目的:
- 开发和评估一种用于自动检测假新闻的新型框架.
- 提高识别伪造内容的准确性和效率.
- 提供强有力的解决方案,防止在线虚假信息的扩散.
主要方法:
- 使用FastText,FastText-Subword和GloVe词嵌入的组合.
- 将这些嵌入式与定制卷积神经网络 (CNN) 集成.
- 采用主要组件分析 (PCA) 来进行特征提取和维度缩小.
主要成果:
- 在假新闻挑战数据集上获得了94.58%的准确性.
- 报告精度为95.35%,回忆率为97.29%,F1得分为96.11%.
- 与各种机器,深度和集体学习方法相比,表现出卓越的性能.
- 在独立的阿拉伯假新闻数据集上验证了有效性.
结论:
- 拟议的框架有效地检测假新闻,具有高准确性和可靠性.
- 将多个词嵌入与CNN集成,为错误信息检测提供了一个强大的方法.
- 该模型在各种数据集上的表现突显了其可概括性和稳定性.
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