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.
- Douglas Aberdeen, Ondrej Pacovsky, and Andrew Slater. The Learning behind Gmail Priority Inbox. In NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds.Google Scholar
- Victoria Bellotti, Nicolas Ducheneaut, Mark Howard, and Ian Smith. Taking Email to Task: The Design and Evaluation of a Task Management Centered Email Tool CHI '03.Google Scholar
- Paul N. Bennett and Jaime Carbonell. Detecting Action-items in E-mail. In SIGIR '05.Google Scholar
- Vitor R. Carvalho and William W. Cohen. On the Collective Classification of Email "Speech Acts" SIGIR '05.Google Scholar
- Marta E. Cecchinato, Abigail Sellen, Milad Shokouhi, and Gavin Smyth. Finding Email in a Multi-Account, Multi-Device World CHI'16.Google Scholar
- Michael Chui, James Manyika, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Hugo Sarrazin, Geoffrey Sands, and Magdalena Westergren 2012. The social economy: Unlocking value and productivity through social technologies. (2012). A report by McKinsey Global Institute.Google Scholar
- William W. Cohen, Vitor R. Carvalho, and Tom M. Mitchell. Learning to Classify Email into Speech Acts. In EMNLP '04.Google Scholar
- Simon Corston-Oliver, Eric Ringger, Michael Gamon, and Richard Campbell. Integration of Email and Task Lists. In First Conference on Email and Anti-Spam.Google Scholar
- Laura A. Dabbish and Robert E. Kraut. Email Overload at Work: An Analysis of Factors Associated with Email Strain CSCW '06.Google Scholar
- Laura A. Dabbish, Robert E. Kraut, Susan Fussell, and Sara Kiesler. Understanding Email Use: Predicting Action on a Message CHI '05.Google Scholar
- Dotan Di Castro, Zohar Karnin, Liane Lewin-Eytan, and Yoelle Maarek. You've Got Mail, and Here is What You Could Do With It!: Analyzing and Predicting Actions on Email Messages. In WSDM '16.Google Scholar
- Danyel Fisher, A. J. Brush, Eric Gleave, and Marc A. Smith. Revisiting Whittaker & Sidner's "Email Overload" Ten Years Later CSCW '06.Google Scholar
- Michael Freed, Jaime G. Carbonell, Geoffrey J. Gordon, Jordan Hayes, Brad A. Myers, Daniel P. Siewiorek, Stephen F. Smith, Aaron Steinfeld, and Anthony Tomasic. RADAR: A Personal Assistant that Learns to Reduce Email Overload AAAI '08.Google Scholar
- David Graus, David van Dijk, Manos Tsagkias, Wouter Weerkamp, and Maarten de Rijke. Recipient Recommendation in Enterprises Using Communication Graphs and Email Content SIGIR '14.Google Scholar
- Mihajlo Grbovic, Guy Halawi, Zohar Karnin, and Yoelle Maarek. How Many Folders Do You Really Need? Classifying Email into a Handful of Categories CIKM '14.Google Scholar
- Ido Guy, Michal Jacovi, Noga Meshulam, Inbal Ronen, and Elad Shahar. Public vs. Private: Comparing Public Social Network Information with Email CSCW '08.Google Scholar
- Minqing Hu and Bing Liu. Mining and Summarizing Customer Reviews. In KDD '04. Google ScholarDigital Library
- Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, Laszlo Lukacs, Marina Ganea, Peter Young, and Vivek Ramavajjala. Smart Reply: Automated Response Suggestion for Email KDD '16.Google Scholar
- Bryan Klimt and Yiming Yang. The Enron Corpus: A New Dataset for Email Classification Research ECML'04.Google Scholar
- Farshad Kooti, Luca Maria Aiello, Mihajlo Grbovic, Kristina Lerman, and Amin Mantrach. Evolution of Conversations in the Age of Email Overload WWW '15.Google Scholar
- Andrew Lampert, Robert Dale, and Cecile Paris. Detecting Emails Containing Requests for Action. HLT '10.Google Scholar
- Carman Neustaedter, A. J. Bernheim Brush, and Marc A. Smith. Beyond "From" and "Received": Exploring the Dynamics of Email Triage CHI EA '05.Google Scholar
- Byung-Won On, Ee-Peng Lim, Jing Jiang, Amruta Purandare, and Loo-Nin Teow. Mining Interaction Behaviors for Email Reply Order Prediction ASONAM '10.Google Scholar
- Ashequl Qadir, Michael Gamon, Patrick Pantel, and Ahmed Hassan Awadallah. Activity Modeling in Email. In NAACL-HLT '16. Google ScholarCross Ref
- S. Radicati. 2014. Email statistics report, 2014--2018. (2014).Google Scholar
- Maya Sappelli, Gabriella Pasi, Suzan Verberne, Maaike de Boer, and Wessel Kraaij 2016. Assessing E-mail intent and tasks in E-mail messages. Inf. Sci. Vol. 358--359 (2016), 1--17.Google ScholarDigital Library
- Michael Gamon Richard Campbell Simon H. Corston-Oliver, Eric Ringger. Task-focused Summarization of Email. In ACL'04.Google Scholar
- Joshua R. Tyler and John C. Tang. When Can I Expect an Email Response? A Study of Rhythms in Email Usage ECSCW'03.Google Scholar
- Steve Whittaker and Candace Sidner. Email Overload: Exploring Personal Information Management of Email CHI '96.Google Scholar
- Yiming Yang and Xin Liu. A Re-examination of Text Categorization Methods. SIGIR '99.Google Scholar
- Ji Zhu, Hui Zou, Saharon Rosset, and Trevor Hastie. 2009. Multi-class AdaBoost. Statistics and Its Interface (2009).Google Scholar
Index Terms
- Characterizing and Predicting Enterprise Email Reply Behavior
Recommendations
Evolution of Conversations in the Age of Email Overload
WWW '15: Proceedings of the 24th International Conference on World Wide WebEmail is a ubiquitous communications tool in the workplace and plays an important role in social interactions. Previous studies of email were largely based on surveys and limited to relatively small populations of email users within organizations. In ...
Characterizing and Predicting Email Deferral Behavior
WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data MiningEmail triage involves going throughunhandled emails and deciding what to do with them. This familiar process can become increasingly challenging as the number of unhandled email grows. During a triage session, users commonlydefer handling emails that ...
Email Reply Prediction: A Machine Learning Approach
Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and ...
Comments