深度学习和句子嵌入用于检测来自在线内容的点击诱新闻
Amara Muqadas1, Hikmat Ullah Khan2, Muhammad Ramzan3
1Department of Computer Science, University of Sargodha, Punjab, Pakistan.
Scientific reports
|April 17, 2025
概括
这项研究引入了一种新的方法,用于通过深度学习和句子嵌入来检测乌尔都语新闻头条中的点击诱. Bi-LSTM模型实现了88%的准确性,显著改善了低资源语言的点击诱识别.
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:
- 用户生成的内容的扩散需要强大的方法来确保在线信息的真实性.
- 旨在最大限度地提高参与度的Clickbait标题对在线新闻的可靠性构成重大挑战.
- 现有的关于点击诱检测的研究主要集中在英语内容上,在低资源语言中留下了一个空白.
研究的目的:
- 开发和评估深度学习模型,用于检测乌尔都语新闻标题中的点击诱.
- 调查句子嵌入作为点击诱识别模型输入特征的有效性.
- 建立乌尔都语clickbait检测的基准,并将深度学习方法与传统方法进行比较.
主要方法:
- 一个精心策划的乌尔都语新闻标题数据集被领域专家创建和标记.
- 最先进的深度特征,特别是句子嵌入,被用作深度学习模型的输入.
- 深度学习模型 (例如Bi-LSTM) 被训练并与使用文本和文字嵌入功能的基线机器学习和集合学习模型进行比较.
- 使用准确度,精度,回忆,F1得分和ROC曲线分析来评估性能.
主要成果:
- 提出的深度学习方法,特别是带有句子嵌入的Bi-LSTM模型,表现出卓越的性能.
- Bi-LSTM模型在识别乌尔都语新闻标题中的点击诱方面取得了最高的88%准确率.
- 这代表了对低资源语言的点击诱检测的重大进步.
结论:
- 深度学习模型,特别是使用句子嵌入时,对于乌尔都语的点击诱检测非常有效.
- 该研究强调了先进的NLP技术的潜力,以应对低资源语言的内容调节挑战.
- 开发的方法和发现为未来的跨语言和低资源点击诱检测研究提供了基础.
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