Understanding email writers: personality prediction from email messages


Shen, J.; Brdiczka, O.; Liu, J. J. Understanding email writers: personality prediction from email messages. Proceedings of the 21st International Conference on User Modeling, Adaptation and Personalization (UMAP); 2013 June 10-14; Rome, Italy. Berlin: Springer; 2013; Lecture Notes in Computer Science 7899: 318-330.


Email is a ubiquitous communication tool and constitutes a significant portion of social interactions. In this paper, we attempt to infer the personality of users based on the content of their emails. Such inference can enable valuable applications such as better personalization, recommendation, and targeted advertising. Considering the private and sensitive nature of email content, we propose a privacy-preserving approach for collecting email and personality data. We then frame personality prediction based on the well-known Big Five personality model and train predictors based on extracted email features. We report prediction performance of 3 generative models with different assumptions. Our results show that personality prediction is feasible, and our email feature set can predict personality with reasonable accuracies.

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