在乌尔都语推文中解决网络欺凌问题:一个全面的数据集和检测系统
Farah Adeeba1, Muhammad Irfan Yousuf1, Izza Anwer2
1Department of Computer Science, University of Engineering and Technology Lahore, Lahore, Punjab, Pakistan.
PeerJ. Computer science
|May 3, 2024
概括
网络欺凌是一个重大问题,有54%的青少年经历过它. 这项研究开发了一个新的数据集,并使用机器学习来检测乌尔都语推文中的网络欺凌,发现快速文本模型最有效.
科学领域:
- 计算语言学 计算语言学
- 社交计算社会计算
- 人工智能的人工智能
背景情况:
- 网络欺凌在青少年中很普遍,包括仇恨言论,威胁和种族主义.
- 现有的网络欺凌检测方法往往缺乏对乌尔都语等低资源语言的具体支持.
研究的目的:
- 为乌尔都语网络欺凌检测创建和发布一个全面的数据集.
- 制定乌尔都语网络欺凌的注释准则.
- 评估机器学习和深度学习模型,以检测乌尔都语推文中的网络欺凌.
主要方法:
- 一个包含12,500条注释的乌尔都语推特的数据集被策划.
- 为乌尔都语网络欺凌制定了注释准则.
- 实施和评估了包括fastText在内的各种机器学习和深度学习模型.
主要成果:
- 与其他评估模型相比,fastText深度学习模型在网络欺凌检测方面表现优异.
- 开发的数据集和方法有效地识别和分类乌尔都语社交媒体内容中的网络欺凌.
结论:
- 这项研究为乌尔都语网络欺凌检测提供了宝贵的资源和有效的模型.
- 这些发现支持通过改善乌尔都语使用者的自动化内容调节来开发更安全的在线环境.
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