文本-图像多式联接模型,用于增强假新闻检测
Szu-Yin Lin1, Yen-Chiu Chen2, Yu-Han Chang3
1Department of Management Science, National Yang Ming Chiao Tung University, Hsinchu.
Science progress
|October 23, 2024
概括
本研究介绍了一种多式联接模型,通过整合文本和图像数据来打击假新闻. 这种方法显著提高了虚假新闻检测的准确性和有效性.
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:
- 互联网和数字技术的普及加剧了区分真实的新闻和伪造的内容的挑战,导致了广泛的错误信息.
- 现有的假新闻检测方法往往单独分析特征,忽视了从多个数据源组合信息的潜在好处.
- 多模式融合提供了更强大的方法,通过整合不同的数据类型,如文本和图像,以获得全面的理解.
研究的目的:
- 开发和评估一种用于虚假新闻识别的新型模型,利用多式联运特征融合.
- 通过整合文字和视觉信息,提高虚假新闻检测的准确性和有效性.
- 通过加强检测框架来解决在线虚假信息的关键问题.
主要方法:
- 设计了一个框架,使用早期,联合和晚期融合策略整合文字和视觉信息.
- 数据在分类之前经过了文字和视觉模式的清理和特征提取.
- 拟议的模型是通过Gossipcop和Fakeddit数据集进行训练和评估的.
主要成果:
- 多式联络融合模型在Gossipcop数据集上实现了85%的高准确率,在Fakeddit数据集上达到90%.
- 该模型表现出强的性能,F1得分为Gossipcop的90%和Fakeddit的88%.
- 在各种培训期间的结果证实了多式联络融合在增强假新闻检测方面的有效性.
结论:
- 综合文本和图像识别的多式融合是打击虚假新闻的高效战略.
- 拟议的框架在发现错误信息的准确性方面取得了重大进展.
- 这项研究强调了全面方法在应对在线假新闻的挑战的重要性.
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