Measuring What Counts: The Case of Rumour Stance Classification

Carolina Scarton, Diego Silva, Kalina Bontcheva

Abstract
Stance classification can be a powerful tool for understanding whether and which users believe in online rumours. The task aims to automatically predict the stance of replies towards a given rumour, namely support, deny, question, or comment. Numerous methods have been proposed and their performance compared in the RumourEval shared tasks in 2017 and 2019. Results demonstrated that this is a challenging problem since naturally occurring rumour stance data is highly imbalanced. This paper specifically questions the evaluation metrics used in these shared tasks. We re-evaluate the systems submitted to the two RumourEval tasks and show that the two widely adopted metrics – accuracy and macro-F1 – are not robust for the four-class imbalanced task of rumour stance classification, as they wrongly favour systems with highly skewed accuracy towards the majority class. To overcome this problem, we propose new evaluation metrics for rumour stance detection. These are not only robust to imbalanced data but also score higher systems that are capable of recognising the two most informative minority classes (support and deny).
Anthology ID:
2020.aacl-main.92
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Kam-Fai Wong, Kevin Knight, Hua Wu
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
925–932
Language:
URL:
https://aclanthology.org/2020.aacl-main.92
DOI:
Bibkey:
Cite (ACL):
Carolina Scarton, Diego Silva, and Kalina Bontcheva. 2020. Measuring What Counts: The Case of Rumour Stance Classification. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 925–932, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Measuring What Counts: The Case of Rumour Stance Classification (Scarton et al., AACL 2020)
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