Testing the Robustness of a BiLSTM-based Structural Story Classifier
This work addresses the reliability of fake news detection models for internet users, but it is incremental as it focuses on noise evaluation of an existing method.
The study tested the robustness of a BiLSTM-based structural classifier for fake news detection by evaluating its performance under noisy labeling conditions, finding that accuracy dropped by 15% when 20% of labels were incorrect.
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).