Automatic detection of stance towards vaccination in online discussion forums

Maria Skeppstedt, Andreas Kerren, Manfred Stede


Abstract
A classifier for automatic detection of stance towards vaccination in online forums was trained and evaluated. Debate posts from six discussion threads on the British parental website Mumsnet were manually annotated for stance ‘against’ or ‘for’ vaccination, or as ‘undecided’. A support vector machine, trained to detect the three classes, achieved a macro F-score of 0.44, while a macro F-score of 0.62 was obtained by the same type of classifier on the binary classification task of distinguishing stance ‘against’ vaccination from stance ‘for’ vaccination. These results show that vaccine stance detection in online forums is a difficult task, at least for the type of model investigated and for the relatively small training corpus that was used. Future work will therefore include an expansion of the training data and an evaluation of other types of classifiers and features.
Anthology ID:
W17-5801
Volume:
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–8
Language:
URL:
https://aclanthology.org/W17-5801
DOI:
Bibkey:
Cite (ACL):
Maria Skeppstedt, Andreas Kerren, and Manfred Stede. 2017. Automatic detection of stance towards vaccination in online discussion forums. In Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017), pages 1–8, Taipei, Taiwan. Association for Computational Linguistics.
Cite (Informal):
Automatic detection of stance towards vaccination in online discussion forums (Skeppstedt et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5801.pdf