@inproceedings{thorne-etal-2017-fake,
title = "Fake news stance detection using stacked ensemble of classifiers",
author = "Thorne, James and
Chen, Mingjie and
Myrianthous, Giorgos and
Pu, Jiashu and
Wang, Xiaoxuan and
Vlachos, Andreas",
editor = "Popescu, Octavian and
Strapparava, Carlo",
booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4214",
doi = "10.18653/v1/W17-4214",
pages = "80--83",
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.",
}
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%0 Conference Proceedings
%T Fake news stance detection using stacked ensemble of classifiers
%A Thorne, James
%A Chen, Mingjie
%A Myrianthous, Giorgos
%A Pu, Jiashu
%A Wang, Xiaoxuan
%A Vlachos, Andreas
%Y Popescu, Octavian
%Y Strapparava, Carlo
%S Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F thorne-etal-2017-fake
%X 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.
%R 10.18653/v1/W17-4214
%U https://aclanthology.org/W17-4214
%U https://doi.org/10.18653/v1/W17-4214
%P 80-83
Markdown (Informal)
[Fake news stance detection using stacked ensemble of classifiers](https://aclanthology.org/W17-4214) (Thorne et al., 2017)
ACL
- James Thorne, Mingjie Chen, Giorgos Myrianthous, Jiashu Pu, Xiaoxuan Wang, and Andreas Vlachos. 2017. Fake news stance detection using stacked ensemble of classifiers. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 80–83, Copenhagen, Denmark. Association for Computational Linguistics.
