停止SpamX:在社交网络中用于垃圾邮件检测的多模式融合方法
Dasari Siva Krishna1, Gorla Srinivas2
1Department of Computer Science and Engineering, Gandhi Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, India.
MethodsX
|March 17, 2025
概括
这项研究涉及社交媒体,特别是Twitter上的垃圾邮件检测,使用深度学习和词嵌入技术. 这项研究旨在通过评估混合融合分类器以提高性能来改善无垃圾邮件环境.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 社交媒体平台产生大量用户上传的内容,包括真实和假的数据.
- 垃圾邮件数据构成了重大挑战,需要有效的检测方法,以保持清洁的在线环境.
- 现有的垃圾邮件检测算法需要改进,以处理社交媒体数据的复杂性.
研究的目的:
- 在Twitter上实施和比较各种用于垃圾邮件检测的算法.
- 评估最先进的词嵌入技术在垃圾邮件检测中的有效性.
- 评估深度学习混合融合分类器,以提高垃圾邮件检测性能.
主要方法:
- 使用了先进的文字嵌入技术:Word2Vec,GloVe和快速文本.
- 开发了两个深度学习混合融合分类器:基于文本的和组合分类器.
- 雇佣的循环神经网络 (RNN) 变体,如门式循环单元 (GRU) 和长期短期记忆 (LSTM),以及卷积神经网络 (CNN).
主要成果:
- 在不同的场景中使用Word2Vec,GloVe和FastText比较结果.
- 评估了结合GRU,LSTM和CNN架构的混合融合分类器.
- 使用F1得分,准确性,回忆和精度等指标评估性能.
结论:
- 这项研究表明了深度学习混合融合方法的潜力,以有效检测垃圾邮件.
- 文字嵌入技术显著影响垃圾邮件检测模型的性能.
- 这些发现表明,这些方法可以提高混合融合垃圾邮件检测系统的整体性能.
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