Emulating Reader Behaviors for Fake News Detection
Junwei Yin Min Gao Kai Shu Zehua Zhao Yinqiu Huang Jia Wang
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.