Predicting Stances from Social Media Posts using Factorization Machines

Akira Sasaki, Kazuaki Hanawa, Naoaki Okazaki, Kentaro Inui


Abstract
Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world. An important step to analyze the discussions on social media and to assist in healthy decision-making is stance detection. This paper presents an approach to detect the stance of a user toward a topic based on their stances toward other topics and the social media posts of the user. We apply factorization machines, a widely used method in item recommendation, to model user preferences toward topics from the social media data. The experimental results demonstrate that users’ posts are useful to model topic preferences and therefore predict stances of silent users.
Anthology ID:
C18-1286
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3381–3390
Language:
URL:
https://aclanthology.org/C18-1286
DOI:
Bibkey:
Cite (ACL):
Akira Sasaki, Kazuaki Hanawa, Naoaki Okazaki, and Kentaro Inui. 2018. Predicting Stances from Social Media Posts using Factorization Machines. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3381–3390, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Predicting Stances from Social Media Posts using Factorization Machines (Sasaki et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1286.pdf