Abstract
In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users are those who can effectively solicit responses from other users. Responsive users are those who are willing to respond to other users. By modeling such behaviors, we are able to mine them and to identify engaging or responsive users. This paper proposes four types of models to quantify engagingness and responsiveness of users. These behaviors can be used as features in email reply order prediction, which predicts the email reply order given an email pair. Our experiments show that engagingness and responsiveness behavior features are more useful than other non-behavioral features in building a classifier for the email reply order prediction task. When combining behavior and non-behavior features, our classifier is also shown to predict the email reply order with good accuracy. This work was extended from the earlier conference paper that appeared in [9].
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Notes
- 1.
We discuss the detailed description of this algorithm in Appendix.
- 2.
In our experiment, we used ε = . 0000001 and numbers of iterations required to compute E RW and R RW are 8 and 12 respectively.
- 3.
See Table 10.4.
- 4.
Recently, [4] reported that email replies often follow the last-in-first-out principle.
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On, BW., Lim, EP., Jiang, J., Teow, LN. (2013). Engagingness and Responsiveness Behavior Models on the Enron Email Network and Its Application to Email Reply Order Prediction. In: Özyer, T., Rokne, J., Wagner, G., Reuser, A. (eds) The Influence of Technology on Social Network Analysis and Mining. Lecture Notes in Social Networks, vol 6. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1346-2_10
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