  Social media becomes the central way for people to obtain and utilise news,
due to its rapidness and inexpensive value of data distribution. Though, such
features of social media platforms also present it a root cause of fake news
distribution, causing adverse consequences on both people and culture. Hence,
detecting fake news has become a significant research interest for bringing
feasible real time solutions to the problem. Most current techniques of fake
news disclosure are supervised, that need large cost in terms of time and
effort to make a certainly interpreted dataset. The proposed framework
concentrates on the text-based detection of fake news items while considering
that only limited number of labels are available. Graphs are functioned
extensively under several purposes of real-world problems on the strength of
their property to structure things easily. Deep neural networks are used to
generate great results within tasks that utilizes graph classification. The
Graph Convolution Network works as a deep learning paradigm which works on
graphs. Our proposed framework deals with limited amount of labelled data; we
go for a semi-supervised learning method. We come up with a semi-supervised
fake news detection technique based on GCN (Graph Convolutional Networks). The
recommended architecture comprises of three basic components: collecting word
embeddings from the news articles in datasets utilising GloVe, building
similarity graph using Word Movers Distance (WMD) and finally applying Graph
Convolution Network (GCN) for binary classification of news articles in
semi-supervised paradigm. The implemented technique is validated on three
different datasets by varying the volume of labelled data achieving 95.27 %
highest accuracy on Real or Fake dataset. Comparison with other contemporary
techniques also reinforced the supremacy of the proposed framework.
