  The growing prevalence of counterfeit stories on the internet has fostered
significant interest towards fast and scalable detection of fake news in the
machine learning community. While several machine learning techniques for this
purpose have emerged, we observe that there is a need to evaluate the impact of
noise on these techniques' performance, where noise constitutes news articles
being mistakenly labeled as fake (or real). This work takes a step in that
direction, where we examine the impact of noise on a state-of-the-art,
structural model based on BiLSTM (Bidirectional Long-Short Term Model) for fake
news detection, Hierarchical Discourse-level Structure for Fake News Detection
by Karimi and Tang (Reference no. 9).
