Entity-Aware Dual Co-Attention Network for Fake News Detection

Sin-Han Yang, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
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引用次数: 1

Abstract

Fake news and misinformation spread rapidly on the Internet. How to identify it and how to interpret the identification results have become important issues. In this paper, we propose a Dual Co-Attention Network (Dual-CAN) for fake news detection, which takes news content, social media replies, and external knowledge into consideration. Our experimental results support that the proposed Dual-CAN outperforms current representative models in two benchmark datasets. We further make in-depth discussions by comparing how models work in both datasets with empirical analysis of attention weights.
基于实体感知的假新闻检测双共注意网络
假新闻和错误信息在互联网上迅速传播。如何识别它以及如何解释识别结果已成为重要问题。在本文中,我们提出了一种用于假新闻检测的双共同注意网络(Dual CAN),该网络考虑了新闻内容、社交媒体回复和外部知识。我们的实验结果支持所提出的双CAN在两个基准数据集中优于当前的代表性模型。我们通过比较模型在两个数据集中的工作方式和注意力权重的实证分析,进一步进行了深入的讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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