Fake News Detection with Deep Diffusive Network Model.
Year: 2018
Type: preprint
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. Based on a detailed data analysis, 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2804688835