FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network

TL;DR

FAKEDETECTOR is a deep diffusive neural network model designed to effectively identify fake news, creators, and subjects in online social networks by leveraging textual features and complex relational data.

Contribution

The paper introduces FAKEDETECTOR, a novel deep diffusive neural network that simultaneously learns representations of news, creators, and subjects for improved fake news detection.

Findings

FAKEDETECTOR outperforms several state-of-the-art models on real-world datasets.

The model effectively captures both explicit and latent features of fake news.

Experimental results demonstrate high accuracy and robustness in fake news detection.

Abstract

In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This paper addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators and subjects. This paper introduces a novel automatic fake news credibility inference model, namely FAKEDETECTOR. Based on a set of explicit and latent features extracted from the textual information, FAKEDETECTOR builds a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously. Extensive experiments have been done on a real-world fake news dataset to compare FAKEDETECTOR with several state-of-the-art models, and the experimental results have demonstrated the effectiveness of the proposed model.