增强对新闻媒体的信任:一种多模式方法,通过社会构造来检测假新闻
Ishfaq Ali1, Muhammad Asif1, Osama Sohaib2,3
1Department of Computer Science, National Textile University, Faisalabad, Punjab, Pakistan.
PloS one
|December 31, 2025
概括
在社交媒体上歪曲新闻的错误引用是一个日益严重的问题. 这项研究引入了一个新的多式联网人工智能框架来检测这些错误引用的新闻文章,增强在线信息完整性.
科学领域:
- 计算社会科学 计算社会科学
- 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:
- 社交媒体上的错误信息挑战了公众的言论和信任.
- 错误地引用合法新闻是错误信息的一个研究不足但至关重要的方面.
- 检测错误引文需要数据集,将原始新闻与社交媒体内容联系起来.
研究的目的:
- 构建一个全面的数据集,对齐新闻故事和社交媒体帖子.
- 开发和评估一个多式联络框架,用于检测引用错误的新闻.
- 分析文本,视觉和上下文特征在错误引文检测中的贡献.
主要方法:
- 在四个现有数据集 (FAKENEWSNET,NELA-GT,TruthSeekers,Twitter15/16) 中进行多源数据三角化.
- 创建了158,400个与社交媒体相关的新闻和帖子对.
- 开发一个综合文本,视觉和上下文特征的多式二元分类模型.
主要成果:
- 一个新的,大规模的数据集用于错误引用研究.
- 一个多式联运分类框架,展示了有效的错误报价检测.
- 除研究揭示了不同模式对检测准确性的影响.
结论:
- 拟议的多式联运框架提供了一种强有力的方法来识别引用错误的新闻.
- 这项工作通过解决社交媒体上的内容扭曲问题,有助于打击错误信息.
- 未来的研究可以建立在数据集和方法上,以加强信息真实性分析.
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