Evolution of Conversations in the Age of Email Overload
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Abstract (arXiv)
Email 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 this paper, we report results of a large-scale study of more than 2 million users exchanging 16 billion emails over several months. We quantitatively characterize the replying behavior in conversations within pairs of users. In particular, we study the time it takes the user to reply to a received message and the length of the reply sent. We consider a variety of factors that affect the reply time and length, such as the stage of the conversation, user demographics, and use of portable devices. In addition, we study how increasing load affects emailing behavior. We find that as users receive more email messages in a day, they reply to a smaller fraction of them, using shorter replies. However, their responsiveness remains intact, and they may even reply to emails faster. Finally, we predict the time to reply, length of reply, and whether the reply ends a conversation. We demonstrate considerable improvement over the baseline in all three prediction tasks, showing the significant role that the factors that we uncover play, in determining replying behavior. We rank these factors based on their predictive power. Our findings have important implications for understanding human behavior and designing better email management applications for tasks like ranking unread emails.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Apr 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2021The impact of virtual mirroring on customer satisfactioncited by 9
- 2025Understanding and Supporting Formal Email Exchange by Answering AI-Generated Questionscited by 6
- 2024From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learningcited by 3
- 2023PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Predictioncited by 2
- 2026Opal: Private Memory for Personal AIcited by 1
- 2026Opal: Private Memory for Personal AI2 Apr 2026
- 2025Understanding and Supporting Formal Email Exchange by Answering AI-Generated Questions6 Feb 2025
- 2024From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning7 Jun 2024
- 2023PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction25 Oct 2023
- 2021The impact of virtual mirroring on customer satisfaction20 May 2021