  Fake News on social media platforms has attracted a lot of attention in
recent times, primarily for events related to politics (2016 US Presidential
elections), healthcare (infodemic during COVID-19), to name a few. Various
methods have been proposed for detecting Fake News. The approaches span from
exploiting techniques related to network analysis, Natural Language Processing
(NLP), and the usage of Graph Neural Networks (GNNs). In this work, we propose
DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying
Fake News. Our approach is a combination of the NLP -- where we encode the news
content, and the GNN technique -- where we encode the Knowledge Graph (KG). A
variety of these encodings provides a complementary advantage to our detector.
We evaluate our framework using two publicly available datasets containing
articles from domains such as politics, business, technology, and healthcare.
As part of dataset pre-processing, we also remove the bias, such as the source
of the articles, which could impact the performance of the models. DEAP-FAKED
obtains an F1-score of 88% and 78% for the two datasets, which is an
improvement of 21%, and 3% respectively, which shows the effectiveness of the
approach.
