Abstract
Several recent studies have shown that textual information of user posts and user behaviors such as liking and sharing the specific posts are useful for predicting the personality of social media users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of textual information on user behaviors for personality prediction. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors. They also show that taking user behaviors into account is crucial for predicting the personality of users who do not post frequently.- Anthology ID:
- P19-2024
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Fernando Alva-Manchego, Eunsol Choi, Daniel Khashabi
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 177–182
- Language:
- URL:
- https://aclanthology.org/P19-2024
- DOI:
- 10.18653/v1/P19-2024
- Cite (ACL):
- Kosuke Yamada, Ryohei Sasano, and Koichi Takeda. 2019. Incorporating Textual Information on User Behavior for Personality Prediction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 177–182, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Incorporating Textual Information on User Behavior for Personality Prediction (Yamada et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P19-2024.pdf