Fake news detection using stacked ensemble of classifiers

Thumbnail
View / Open Files
Authors
Thorne, J
Chen, M
Myrianthous, G
Pu, J
Wang, X
  Vlachos, Andreas
Publication Date
2017
Journal Title
EMNLP 2017 - 2nd Workshop on Natural Language Processing Meets Journalism, NLPmJ 2017 - Proceedings of the Workshop
Conference Name
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
ISBN
9781945626883
Publisher
Association for Computational Linguistics
Pages
80-83
Type
Conference Object
This Version
VoR
Metadata
Show full item record
Citation
Thorne, J., Chen, M., Myrianthous, G., Pu, J., Wang, X., & Vlachos, A. (2017). Fake news detection using stacked ensemble of classifiers. EMNLP 2017 - 2nd Workshop on Natural Language Processing Meets Journalism, NLPmJ 2017 - Proceedings of the Workshop, 80-83. https://doi.org/10.18653/v1/w17-4214
Abstract
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.
Identifiers
External DOI: https://doi.org/10.18653/v1/w17-4214
This record's URL: https://www.repository.cam.ac.uk/handle/1810/332659
Rights
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/
Statistics



Total file downloads (since January 2020). For more information on metrics see the IRUS guide.

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International