在发布之前检测和确定删除推文的原因
Hamdy Mubarak1, Samir Abdaljalil1, Azza Nassar2
1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Frontiers in artificial intelligence
|October 16, 2023
概括
研究人员开发了模型来预测被删除的社交媒体帖子及其原因,改进了内容调节. 这项研究的重点是识别删除的阿拉伯语推特的细粒度类别,提高在线安全.
科学领域:
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
- 社交媒体分析 社交媒体分析
背景情况:
- 社交媒体平台促进信息共享,但容易被滥用,包括仇恨言论和错误信息.
- 平台或用户删除内容是常见的,但删除的细致原因未得到充分研究.
- 现有的研究往往缺乏删除内容的详细分类,特别是在非英语语境.
研究的目的:
- 解决了解删除社交媒体内容细粒度原因的差距.
- 根据特定的虚假信息类别识别和分类删除的阿拉伯语推特.
- 开发用于识别删除帖子及其删除理由的预测模型.
主要方法:
- 策划了4万条阿拉伯语推特的数据集,用粗略和细粒度标签进行注释.
- 设计和实施机器学习模型,包括经典和基于变压器的方法.
- 评估模型性能与基线方法对比,用于预测删除概率和原因.
主要成果:
- 与大多数基线相比,模型实现了显著的性能改进,细粒度标签的绝对改进率为25%.
- 根据详细的错误信息标准,成功识别和分类删除的阿拉伯语推特.
- 证明了预测模型在理解内容删除模式方面的有效性.
结论:
- 开发的模型可以有效地预测社交媒体帖子被删除的可能性及其潜在原因.
- 这项研究通过提供用于早期检测有问题的内容的工具,有助于更好的内容调节策略.
- 这些发现特别适用于在阿拉伯语语境中调节内容,提高平台安全性.
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