检测健康错误信息:在分类任务中对机器学习和图形卷积网络进行比较分析
Bharti Khemani1, Shruti Patil2, Ketan Kotecha2
1Symbiosis Institute of Technology, Symbiosis International (Deemed University, Pune, India.
MethodsX
|May 22, 2024
概括
检测在线健康错误信息至关重要. 与TF-IDF嵌入的图形卷积网络 (GCN) 在识别虚假健康信息方面取得了最高的准确性 (93.86%),超过了传统模型.
科学领域:
- 数字健康数字健康
- 计算语言学 计算语言学
- 信息科学 信息科学 信息科学
背景情况:
- 数字时代带来了巨大的挑战,因为健康错误信息在网上广泛传播.
- 这种错误信息对公众健康和福祉构成重大威胁.
- 有效检测健康错误信息对于保护公共健康至关重要.
研究的目的:
- 进行各种分类模型的比较分析,以检测健康错误信息.
- 评估传统机器学习算法的性能与先进的图形卷积网络 (GCNs) 相比.
- 确定最有效的算法方法来打击虚假健康信息的传播.
主要方法:
- 进行了对分类算法的比较分析.
- 评估的算法包括被动侵略分类器,随机森林,决策树,后勤回归,轻型GBM,GCN,GCN与BERT,GCN与TF-IDF,以及GCN与Word2Vec.
- 使用准确性,精确性,回忆和F1分数等指标来评估性能.
主要成果:
- 与TF-IDF嵌入相结合的图形卷积网络 (GCN) 显示出卓越的性能.
- 使用TF-IDF的GCN实现了93.86%的最高精度.
- 其他模型显示出不同程度的有效性,随机森林为86%,被动侵略分类器为85.75%.
结论:
- 图形卷积网络,特别是使用TF-IDF嵌入时,是检测健康错误信息的高效方法.
- 这些发现为开发强大的系统来打击在线健康错误信息提供了有价值的见解.
- 这项研究强调了先进的基于网络的模型在解决关键公共卫生信息挑战方面的潜力.
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