Abstract
Nowadays, mailing lists are widely used in team work for discussion and consultation. Identifying important emails in mailing list discussions could significantly benefit content summary and opinion leader recognition. However, previous studies only focus on the importance evaluation methods regarding personal emails, and there is no consensus on the definition of important emails. Therefore, in this paper we consider the characteristics of mailing lists and study how to evaluate email importance in mailing list discussions. Our contribution mainly includes the following aspects. First, we propose ER-Match, an email conversation thread reconstruction algorithm that takes nested quotation relationships into consideration while constructing the email relationship network. Based on the email relationship network, we formulate the importance of emails in mailing list discussions. Second, we propose a feature-rich learning method to predict the importance of new emails. Furthermore, we characterize various factors affecting email importance in mailing list discussions. Experiments with publicly available mailing lists show that our prediction model outperforms baselines with large gains.
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Notes
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The body here refers to contents without header, signature and quotation.
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For email importance prediction with XGBoost, we set learning_rate = 0.1, n_estimators = 1000, max_depth = 5, min_child_weight = 1, gamma = 0, subsample = 0.8, colsample_bytree = 0.8, objective = ’binary:logistic’, scale_pos_weight = 1, seed = 27.
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This work is supported by National Key Research & Development Program (2016YFB1000503).
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Jiang, K., Hu, C., Sun, J., Shen, Q., Jiang, X. (2019). Email Importance Evaluation in Mailing List Discussions. In: Hacid, H., Sheng, Q., Yoshida, T., Sarkheyli, A., Zhou, R. (eds) Data Quality and Trust in Big Data. QUAT 2018. Lecture Notes in Computer Science(), vol 11235. Springer, Cham. https://doi.org/10.1007/978-3-030-19143-6_3
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