MEFaND: A Multimodel Framework for Early Fake News Detection

Abstract

Alongside social media platforms’ rise in popularity, fake news circulation has increased, highlighting the need for more practical methods to detect this phenomenon. The constantly evolving format of fake news makes it difficult for approaches that rely on a single modality of news to generalize the different types of false news. Furthermore, earlier approaches require extensive propagation data to determine the veracity of news, which can be challenging to collect in the early stages of news dissemination. Thus, we propose a multimodal early fake news detection approach that leverages latent insights into both news content and propagation knowledge. We design a multimodule architecture using graph neural networks (GNNs) to represent edge-enhanced and node-enhanced propagation graphs and bidirectional encoder representations from transformers (BERTs) to generate contextualized representations of news content. Our approach tackles the challenge of early detection in a more realistic scenario, accessing early propagation data in a single social media post and short-length news content. Moreover, we conduct comprehensive studies on user characteristics using statistical techniques to identify attributes with strong discriminative capability for identifying false news. We also analyze temporal and structural properties of fake news propagation graphs to demonstrate distinguishable patterns of false and real news behavior. Our model outperforms several state-of-the-art methods, achieving an impressive F1-score of 99% and 96% on two public datasets. The individual contribution of various components in our model to the final performance is also measured, which can be insightful for future research on multimodal false news detection.