在社交媒体上检测谣言,使用基于情感的深度学习方法.
Drishti Sharma1, Abhishek Srivastava1
1Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India.
PeerJ. Computer science
|September 24, 2024
概括
本研究介绍了SEMTEC,这是一种用于在社交媒体上检测谣言的深度学习模型. 通过分析推特的情绪和情绪,SEMTEC实现了92%的准确性,改进了现有的方法.
科学领域:
- 计算语言学 计算语言学
- 社交媒体分析 社交媒体分析
- 人工智能的人工智能
背景情况:
- 社交媒体平台是信息传播的主要道.
- 社交媒体上的未经验证的信息和谣言可能会对社会造成重大伤害.
- 现有的谣言检测方法通常依赖于功能工程或深度学习,而不完全探索情感和情感线索.
研究的目的:
- 开发一种先进的深度学习模型,用于准确检测谣言.
- 研究情感和情感分析对改善谣言检测的影响.
- 提出和验证一种新的方法,将内容,情感和情感整合到谣言分类中.
主要方法:
- 利用深度学习方法来分析推文的情感和情感方面.
- 开发了感觉和情感驱动的转换器分类器 (SEMTEC) 方法.
- SEMTEC提取了情感和情感标签,将它们与主要推特的基于内容的信息集成为语义分析.
主要成果:
- 在PHEME数据集上检测谣言的准确率达到了92%.
- 在一个新的数据集上验证了SEMTEC方法,Twitter24.
- 在Twitter24数据集上,SEMTEC的准确性比标准方法高出2%.
结论:
- 分析用户的情绪状态和情绪显著提高了谣言检测能力.
- 通过结合多式联络信息,SEMTEC提供了一种强大而准确的方法来识别社交媒体上的谣言.
- 拟议的方法显示出对打击错误信息的现实应用有前途.
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