Fake news stance detection using stacked ensemble of classifiers
James Thorne | Mingjie Chen | Giorgos Myrianthous | Jiashu Pu | Xiaoxuan Wang | Andreas Vlachos
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
Fake news has become a hotly debated topic in journalism. In this paper, we present our entry to the 2017 Fake News Challenge which models the detection of fake news as a stance classification task that finished in 11th place on the leader board. Our entry is an ensemble system of classifiers developed by students in the context of their coursework. We show how we used the stacking ensemble method for this purpose and obtained improvements in classification accuracy exceeding each of the individual models’ performance on the development data. Finally, we discuss aspects of the experimental setup of the challenge.