识别多式联运错误信息,利用新奇性检测和情绪识别
Rina Kumari1, Nischal Ashok2, Pawan Kumar Agrawal3
1School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Odisha Bhubaneswar, India.
概括
这项研究引入了一个新的数据集和框架来检测多式联络假新闻,即使用真实媒体与虚假文本. 该方法利用监督对比学习来检测新奇事物和预测情绪,以提高准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 多模式假新闻,使用真实媒体与操纵文本,在社交媒体上普遍存在.
- 检测这种误导性内容是具有挑战性的,因为它依赖于外部背景和新的,唤起情感的元素.
研究的目的:
- 为了应对检测多式联运虚假新闻的挑战.
- 引入一个新的数据集和一个多式联运框架,以加强假新闻的检测.
主要方法:
- 开发了一个新的多式联络虚假新闻数据集,结合了来自真实的来源的背景知识.
- 设计了一种采用监督对比学习 (SCL) 的多式模式框架,用于新奇性检测和情绪预测任务.
主要成果:
- 与现有的最先进的 (SOTA) 模型相比,拟议的框架在检测多式联络虚假新闻方面表现优越.
- 广泛的实验验证了基于SCL的新奇发现和情绪预测组件的有效性.
结论:
- 新的数据集和多式联运框架为识别复杂的在线错误信息形式提供了一个有希望的解决方案.
- 整合背景知识和分析内容的新性和情感对于强大的假新闻检测至关重要.
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