Fake news detection has been a more urgent technical demand for operators of online
social platforms, and the prevalence of deep learning well boosts its development. From the …

Amid the proliferation of misinformation on social networks, automated rumor detection has
emerged as a pivotal and pressing research domain. Nonetheless, current methodologies …

In the trend of the accelerated progression of communication network technology, the
emergence of virtual communities (VCs), virtual societies (VSs), metaverse, and other …

To defend against fake news, researchers have developed various methods based on texts.
These methods can be grouped as 1) pattern-based methods, which focus on shared …

False news that spreads on social media has proliferated over the past years and has led to
multi-aspect threats in the real world. While there are studies of false news on specific …

Multi-modal fake news detection has drawn considerable attention with the development of
online social media. Existing methods primarily conduct direct cross-modal fusion, while …

The existing approaches based on different neural networks automatically capture and fuse
the multimodal semantics of news, which have achieved great success for fake news …

With the rise of social media, the spread of fake news has become a significant concern,
potentially misleading public perceptions and impacting social stability. Although deep …

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

Neural text transfer aims to change the style of a text sequence while kee** its original
content. Due to the lack of parallel data, unsupervised learning-based approaches have …