论文标题
用于假新闻检测的自适应互动融合网络
Adaptive Interaction Fusion Networks for Fake News Detection
论文作者
论文摘要
虚假新闻检测的大多数方法普遍着重于学习和融合各种检测功能。但是，学习各种特征是独立的，这导致社交媒体上的功能之间缺乏交叉交流融合，尤其是在帖子和评论之间。通常，在虚假新闻中，帖子和评论之间存在情感上的联系和语义冲突。如何表示和融合两者之间的跨界是一个关键挑战。在本文中，我们提出了自适应互动融合网络（AIFN），以实现虚假新闻检测功能之间的跨界融合。在AIFN中，要发现语义冲突，我们设计了封闭的自适应互动网络（增益），以捕获帖子和评论之间的适应性相似语义和矛盾的语义。为了建立特征关联，我们设计了语义级融合自我发项网络（SFSN），以增强特征之间的语义相关性和融合。在两个现实世界数据集（即Rumoureval和Pheme）上进行了广泛的实验表明，AIFN可实现最先进的性能，并将精度分别提高了2.05％以上和1.90％。
The majority of existing methods for fake news detection universally focus on learning and fusing various features for detection. However, the learning of various features is independent, which leads to a lack of cross-interaction fusion between features on social media, especially between posts and comments. Generally, in fake news, there are emotional associations and semantic conflicts between posts and comments. How to represent and fuse the cross-interaction between both is a key challenge. In this paper, we propose Adaptive Interaction Fusion Networks (AIFN) to fulfill cross-interaction fusion among features for fake news detection. In AIFN, to discover semantic conflicts, we design gated adaptive interaction networks (GAIN) to capture adaptively similar semantics and conflicting semantics between posts and comments. To establish feature associations, we devise semantic-level fusion self-attention networks (SFSN) to enhance semantic correlations and fusion among features. Extensive experiments on two real-world datasets, i.e., RumourEval and PHEME, demonstrate that AIFN achieves the state-of-the-art performance and boosts accuracy by more than 2.05% and 1.90%, respectively.
