Fake news detection aims to detect fake news widely spreading on social media platforms, which can negatively influence the public and the government. Many approaches have been developed to exploit relevant information from news images, text, or videos. However, these methods may suffer from the following limitations: (1) ignore the inherent emotional information of the news, which could be beneficial since it contains the subjective intentions of the authors; (2) pay little attention to the relation (similarity) between the title and textual information in news articles, which often use irrelevant title to attract reader' attention. To this end, we propose a novel Title-Text similarity and emotion-aware Fake news detection (TieFake) method by jointly modeling the multi-modal context information and the author sentiment in a unified framework. Specifically, we respectively employ BERT and ResNeSt to learn the representations for text and images, and utilize publisher emotion extractor to capture the author's subjective emotion in the news content. We also propose a scale-dot product attention mechanism to capture the similarity between title features and textual features. Experiments are conducted on two publicly available multi-modal datasets, and the results demonstrate that our proposed method can significantly improve the performance of fake news detection. Our code is available at https://github.com/UESTC-GQJ/TieFake.

翻译：假新闻检测旨在检测社交媒体平台上广泛传播的虚假新闻，这些新闻可能对公众和政府产生负面影响。目前已开发出多种方法，利用新闻图像、文本或视频中的相关信息。然而，这些方法可能面临以下局限：（1）忽略新闻中固有的情感信息，而该信息由于包含作者的主观意图，可能具有积极作用；（2）较少关注新闻标题与文本信息之间的关系（相似度），而新闻常通过不相关的标题吸引读者注意。为此，我们提出一种新颖的标题-文本相似度与情感感知假新闻检测方法（TieFake），通过联合建模多模态上下文信息与作者情感，将其整合至统一框架。具体而言，我们分别采用BERT和ResNeSt学习文本与图像的表示，并利用发布者情感提取器捕获新闻内容中作者的主观情感。此外，我们还提出了一种缩放点积注意力机制，用于捕捉标题特征与文本特征之间的相似度。实验在两个公开可用的多模态数据集上进行，结果表明，我们提出的方法能显著提升假新闻检测的性能。我们的代码开源在 https://github.com/UESTC-GQJ/TieFake。