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
- Editors:
- Octavian Popescu, Carlo Strapparava
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 84–89
- Language:
- URL:
- https://aclanthology.org/W17-4215
- DOI:
- 10.18653/v1/W17-4215
- Cite (ACL):
- Peter Bourgonje, Julian Moreno Schneider, and Georg Rehm. 2017. From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 84–89, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles (Bourgonje et al., 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W17-4215.pdf