在Twitter/X上使用机器学习和深度学习模型对有关COVID-19的健康相关信息进行分类和事实检查
Elham Sharifpoor1, Maryam Okhovati2, Mostafa Ghazizadeh-Ahsaee3
1Medical Library and Information Sciences Department, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
BMC medical informatics and decision making
|February 11, 2025
概括
TextConvoNet有效地检测到Twitter/X上的健康错误信息. 这种深度学习模型在分类可信与不可信的COVID-19健康信息方面取得了高准确性,优于其他方法.
科学领域:
- 计算语言学计算语言学
- 医疗信息学 医疗信息学
- 机器学习是机器学习.
背景情况:
- 社交媒体上的健康信息在发现错误信息方面提出了独特的挑战.
- 需要强大的事实核查模型来打击健康错误信息的传播.
- 以前的方法需要进一步的研究,以有效地分析健康含量.
研究的目的:
- 确定在Twitter/X上检测和分类可靠与错误信息健康内容的最有效方法.
- 为了比较各种机器学习和深度学习模型的性能,以检测健康错误信息.
- 分析与Twitter/X.共享的COVID-19相关的健康信息.
主要方法:
- 利用七种不同的机器学习/深度学习模型进行分析.
- 收集,处理,并将Twitter/X数据标记为"值得信赖的信息"和"错误信息"数据集.
- 在类平衡和评估模型中使用共弦相似性,使用准确度,精度,回忆,F1分数,ROC曲线和AUC.
主要成果:
- 文本ConvoNet表现出卓越的性能,平均准确度,精度,回忆率和F1得分超过90%.
- 该模型实现了高精度 (85%) 和F1得分 (89%) 的可靠信息.
- 文本ConvoNet在识别错误信息方面表现出色,准确率为94%,F1得分为91%.
结论:
- 文本ConvoNet是检测和分类可信与错误信息健康内容在Twitter/X最有效的模型.
- 该研究强调了深度学习模型在打击健康错误信息方面的潜力.
- 研究结果为未来的健康事实核查系统提供了坚实的框架.
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