There has been significant interest in integrating sentiment analysis with graph neural
networks (GNNs) for stock prediction tasks. This article thoroughly reviews the application of …

Fake news is frequently disseminated through social media, which significantly impacts
public perception and individual decision-making. Accurate identification of fake news on …

In recent years, advanced machine learning and artificial intelligence techniques have
gained popularity due to their ability to solve problems across various domains with high …

Large language models are limited by challenges in factuality and hallucinations to be
directly employed off-the-shelf for judging the veracity of news articles, where factual …

Fraud detection is a vital and challenging task in the financial domain. Traditional machine
learning approaches have limitations in dealing with the high imbalance and complexity of …

The proliferation of social media platforms has fueled the rapid dissemination of fake news,
posing threats to our real-life society. Existing methods use multimodal data or contextual …

In recent years, generative artificial intelligence models, represented by Large Language
Models (LLMs) and Diffusion Models (DMs), have revolutionized content production …

Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web
applications such as social networks. Yet, efficient GNN serving remains a critical challenge …

The creation and propagation of disinformation on social media is a growing concern. The
widespread dissemination of disinformation can have destructive effects on people's …

Graph Neural Network (GNN)-based fake news detectors apply various methods to construct
graphs, aiming to learn distinctive news embeddings for classification. Since the …