  The rising growth of fake news and misleading information through online
media outlets demands an automatic method for detecting such news articles. Of
the few limited works which differentiate between trusted vs other types of
news article (satire, propaganda, hoax), none of them model sentence
interactions within a document. We observe an interesting pattern in the way
sentences interact with each other across different kind of news articles. To
capture this kind of information for long news articles, we propose a graph
neural network-based model which does away with the need of feature engineering
for fine grained fake news classification. Through experiments, we show that
our proposed method beats strong neural baselines and achieves state-of-the-art
accuracy on existing datasets. Moreover, we establish the generalizability of
our model by evaluating its performance in out-of-domain scenarios. Code is
available at https://github.com/MysteryVaibhav/fake_news_semantics
