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Characterizing and Predicting Enterprise Email Reply Behavior

Published:07 August 2017Publication History

ABSTRACT

Email is still among the most popular online activities. People spend a significant amount of time sending, reading and responding to email in order to communicate with others, manage tasks and archive personal information. Most previous research on email is based on either relatively small data samples from user surveys and interviews, or on consumer email accounts such as those from Yahoo! Mail or Gmail. Much less has been published on how people interact with enterprise email even though it contains less automatically generated commercial email and involves more organizational behavior than is evident in personal accounts. In this paper, we extend previous work on predicting email reply behavior by looking at enterprise settings and considering more than dyadic communications. We characterize the influence of various factors such as email content and metadata, historical interaction features and temporal features on email reply behavior. We also develop models to predict whether a recipient will reply to an email and how long it will take to do so. Experiments with the publicly-available Avocado email collection show that our methods outperform all baselines with large gains. We also analyze the importance of different features on reply behavior predictions. Our findings provide new insights about how people interact with enterprise email and have implications for the design of the next generation of email clients.

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        • Published in

          cover image ACM Conferences
          SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
          August 2017
          1476 pages
          ISBN:9781450350228
          DOI:10.1145/3077136

          Copyright © 2017 ACM

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          Publication History

          • Published: 7 August 2017

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          SIGIR '17 Paper Acceptance Rate78of362submissions,22%Overall Acceptance Rate792of3,983submissions,20%

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