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TLFND:基于三级特征匹配距离的多模式融合模型,用于假新闻检测
Junda Wang1,2, Jeffrey Zheng2, Shaowen Yao1
1Engineering Research Center of Cyberspace, Yunnan University, Kunming 650091, China.
Entropy (Basel, Switzerland)
|November 24, 2023
概括
这项研究引入了一种新的多式联络融合模型 (TLFND) 来检测假新闻,通过整合文本和图像数据来提高准确性. 该模型在真实世界数据集上显示了显著的性能增长.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 人工智能的人工智能
背景情况:
- 假新闻传播迅速且廉价,这给自动检测带来了挑战.
- 现有的方法与多式联运新闻内容和数据限制作斗争.
研究的目的:
- 开发一种新的多式联接融合模型,用于增强假新闻检测.
- 解决当前关于多式联运数据的假新闻检测方法的局限性.
主要方法:
- 引入了两级假新闻检测 (TLFND) 模型.
- 采用三级特征匹配距离方法,整合文本 (标题,体) 和图像数据.
- 利用切比舍夫距离和一个适应性的进化算法来计算损失函数.
主要成果:
- 在三个公共数据集 (PolitiFact,GossipCop,Twitter) 中,TLFND显示出有效性.
- 在所有四个评估指标中都取得了显著的改进.
- 这导致F1分数分别增加了6.6%,2.9%和2.3%.
结论:
- 拟议的TLFND模型显著提高了假新闻检测能力.
- 多模式融合,包括文本和图像,对于准确的假新闻识别至关重要.
- 切比舍夫距离度量和自适应进化算法有助于模型的有效性.
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