Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

The proliferation of fake news and fake profiles on social media platforms poses significant
threats to information integrity and societal trust. Traditional detection methods, including …

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 …

With the rapid advancement of social media platforms like Weibo and WeChat, alongside
the emergence of deepfake technologies, tackling fake information has become a major …

An increasing demand for model explainability has accompanied the widespread adoption
of transformers in various fields of applications. In this paper, we conduct a survey of the …

Online misinformation spreads rapidly, demanding automated rumour detection. BERT
embeddings show promise for microblogging rumour classification, capturing contextual …