The remarkable success of Transformer-based embeddings in natural language tasks has
sparked interest among researchers in applying them to classify rumours on social media …

Multimodal fake news detection, which aims to detect fake news across vast amounts of
multimodal data in social networks, greatly contributes to identifying potential risks on the …

Rumour veracity on social networks aims to determine the authenticity of a claim based on
the conversation graph. Current works typically employ graph neural networks (GNNs) to …

Social media and user-generated content (UGC) have become increasingly important
features of journalistic work in a number of different ways. However, the growth of …

Malicious online rumors with high popularity, if left undetected, can spread very quickly with
damaging societal implications. The development of reliable computational methods for …

Stance detection, as the task of determining the viewpoint of a social media post towards a
target as' favor'or'against', has been understudied in the challenging yet realistic scenario …

Previous studies have highlighted the importance of vaccination as an effective strategy to
control the transmission of the COVID-19 virus. It is crucial for policymakers to have a …

The automatic detection of multimodal fake news has recently garnered significant attention.
However, the existing detection methods mainly focus on merging textual and visual …

A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to
detect emerging, previously unknown rumors. Past research has indicated that content …

In e-commerce recommendation systems, users' long-term and short-term interests jointly
influence product selection. However, the behavioral conformity phenomenon tends to be …