The wide dissemination of fake news has affected our lives in many aspects, making fake news detection important and attracting increasing attention. Existing approaches make substantial contributions in this field by modeling news from a single-modal or multi-modal perspective. However, these modal-based methods can result in sub-optimal outcomes as they ignore reader behaviors in news consumption and authenticity verification. For instance, they haven't taken into consideration the component-by-component reading process: from the headline, images, comments, to the body, which is essential for modeling news with more granularity. To this end, we propose an approach of Emulating the behaviors of readers (Ember) for fake news detection on social media, incorporating readers' reading and verificating process to model news from the component perspective thoroughly. Specifically, we first construct intra-component feature extractors to emulate the behaviors of semantic analyzing on each component. Then, we design a module that comprises inter-component feature extractors and a sequence-based aggregator. This module mimics the process of verifying the correlation between components and the overall reading and verification sequence. Thus, Ember can handle the news with various components by emulating corresponding sequences. We conduct extensive experiments on nine real-world datasets, and the results demonstrate the superiority of Ember.

翻译：假新闻的广泛传播已在诸多方面影响我们的生活，这使得假新闻检测变得重要且日益受到关注。现有方法通过单模态或多模态角度建模新闻，在这一领域做出了重要贡献。然而，这类基于模态的方法忽略了读者在新闻消费与真实性验证中的行为，可能导致次优结果。例如，它们未能考虑从标题、图像、评论到正文的逐组件阅读过程，而这一过程对于以更高粒度建模新闻至关重要。为此，我们提出一种模拟读者行为的方法（Ember），用于社交媒体上的假新闻检测，该方法融合读者的阅读与验证过程，从组件视角全面建模新闻。具体而言，我们首先构建组件内特征提取器，模拟对每个组件进行语义分析的行为；随后设计一个包含组件间特征提取器与基于序列的聚合器的模块，该模块模拟验证组件间相关性以及整体阅读与验证序列的过程。因此，Ember能够通过模拟相应序列处理包含多种组件的新闻。我们在九个真实世界数据集上进行了广泛实验，结果证明了Ember的优越性。