Simple Open Stance Classification for Rumour Analysis

Ahmet Aker, Leon Derczynski, Kalina Bontcheva


Abstract
Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.
Anthology ID:
R17-1005
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
31–39
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_005
DOI:
10.26615/978-954-452-049-6_005
Bibkey:
Cite (ACL):
Ahmet Aker, Leon Derczynski, and Kalina Bontcheva. 2017. Simple Open Stance Classification for Rumour Analysis. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 31–39, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Simple Open Stance Classification for Rumour Analysis (Aker et al., RANLP 2017)
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PDF:
https://doi.org/10.26615/978-954-452-049-6_005