模式感知基于学习的决定性因素发现,用于多模式假新闻检测
IEEE transactions on neural networks and learning systems
|September 20, 2024
概括
新的MoPeD模型通过分析文本和图像特征来有效检测假新闻,解决当前方法的局限性. 它通过关注模式异质性和发现关键决定性因素来增强虚假新闻的检测能力.
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:
- 假新闻的传播对公众安全和社会论构成重大风险.
- 现有的多模式假新闻检测方法往往忽视模式异质性,限制其识别关键决定性信息的能力.
- 需要先进的模型,能够有效地处理虚假新闻文章中的各种信息.
研究的目的:
- 提出一种新的模型,一种基于感知学习的定性因素发现 (MoPeD) 的模式,用于增强假新闻检测.
- 通过关注模式异质性和提取决定性信息来解决现有方法的局限性.
- 提高虚假新闻检测系统的准确性和稳定性.
主要方法:
- MoPeD模型集成了一个双编码模块,结合了对比性语言图像预训 (CLIP) 编码器和模态特定编码器.
- 采用多层跨模式融合模块来处理模式异质性,并理解文本和图像之间的隐含含义.
- 一个模式感知学习模块动态强调基于跨模式内容异质性得分的决定性特征.
主要成果:
- 在三个公共数据集上的实验评估表明,MoPeD模型的优越性超过了最先进的假新闻检测方法.
- 该模型有效地从单模和多模特征中提取决定因素.
- 通过考虑模式特定和跨模式信息,MoPeD在识别假新闻方面表现得更好.
结论:
- 拟议的MoPeD模型通过有效解决模式异质性,在假新闻检测方面取得了重大进展.
- 模式感知学习方法允许对决定性特征进行适应性强调,从而实现更准确的检测.
- MoPeD提供了一个强大的框架,用于发现假新闻中的决定性因素,优于现有的方法.
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