The prevalence and perniciousness of fake news has been a critical issue on the Internet,
which stimulates the development of automatic fake news detection in turn. In this paper, we …

Fake news detection is crucial for preventing the dissemination of misinformation on social
media. To differentiate fake news from real ones, existing methods observe the language …

The prevalence and perniciousness of fake news have been a critical issue on the Internet,
which stimulates the development of automatic fake news detection in turn. In this paper, we …

The existing models for multi-modal fake news detection focus mainly on capturing common
similar semantics between different modalities to improve detection performance. However …

Given a natural language statement, how to verify its veracity against a large-scale textual
knowledge source like Wikipedia? Most existing neural models make predictions without …

The opaqueness of the multi-hop fact verification model imposes imperative requirements
for explainability. One feasible way is to extract rationales, a subset of inputs, where the …

Automatic detection of fake news has received widespread attentions over recent years. A
pile of efforts has been put forward to address the problem with high accuracy, while most of …

Evidence-aware fake news detection aims to conduct reasoning between news and
evidences, which are retrieved based on news content, to find uniformity or inconsistency …

Given a possibly false claim sentence, how can we automatically correct it with minimal
editing? Existing methods either require a large number of pairs of false and corrected …

This paper reviews and summarizes the research results on fact-based fake news from the
perspectives of tasks and problems, algorithm strategies, and datasets. First, the paper …