From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles

Peter Bourgonje, Julian Moreno Schneider, Georg Rehm


Abstract
We present a system for the detection of the stance of headlines with regard to their corresponding article bodies. The approach can be applied in fake news, especially clickbait detection scenarios. The component is part of a larger platform for the curation of digital content; we consider veracity and relevancy an increasingly important part of curating online information. We want to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines. On a publicly available data set annotated for the stance of headlines with regard to their corresponding article bodies, we achieve a (weighted) accuracy score of 89.59.
Anthology ID:
W17-4215
Volume:
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
84–89
Language:
URL:
https://www.aclweb.org/anthology/W17-4215
DOI:
10.18653/v1/W17-4215
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4215.pdf