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 cooccurrence 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.
CITATION STYLE
Yamada, K., Sasano, R., & Takeda, K. (2019). Incorporating textual information on user behavior for personality prediction. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop (pp. 177–182). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p19-2024
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