深度学习用于打击多类别文本内容中的错误信息
Rafał Kozik1, Wojciech Mazurczyk2, Krzysztof Cabaj2
1Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究引入了一个新的分类委员会,以打击在线虚假信息和假新闻. 整体方法增强了模型的概括性,以便在现实场景中更有效地检测假新闻.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 社交计算社会计算
背景情况:
- 社交媒体的兴起模糊了专家和非专家的意见,影响了传统媒体.
- 社交平台上的虚假信息和假新闻对国家安全构成重大威胁.
- 现有的深度学习解决方案用于假新闻检测,由于数据缺陷,与现实世界的模型概括性作斗争.
研究的目的:
- 提出一种创新的解决方案,用一组分类器来检测假新闻.
- 在现实世界应用中解决模型概括的挑战.
- 开发有效的工具来打击在线虚假信息的传播.
主要方法:
- 开发了一组用于检测假新闻的分类器.
- 使用多标签文本类别分类来制定合集.
- 在独特的子公司上独立训练各种基本模型.
主要成果:
- 在六个基准数据集上进行的实验表明了有希望的结果.
- 拟议的分类机构委员会方法在检测假新闻方面表现出有效性.
- 这些发现表明了未来研究打击在线虚假信息的可行方向.
结论:
- 分类委员会提供了一种有希望的方法来提高假新闻的检测.
- 解决数据缺陷和改进模型概括对于现实应用至关重要.
- 这项研究为开发针对恶意虚假信息活动的强大工具开辟了道路.
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