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Understanding re-finding behavior in naturalistic email interaction logs

Published: 24 July 2011 Publication History

Abstract

In this paper we present a longitudinal, naturalistic study of email behavior (n=47) and describe our efforts at isolating re-finding behavior in the logs through various qualitative and quantitative analyses. The presented work underlines the methodological challenges faced with this kind of research, but demonstrates that it is possible to isolate re-finding behavior from email interaction logs with reasonable accuracy. Using the approaches developed we uncover interesting aspects of email re-finding behavior that have so far been impossible to study, such as how various features of email-clients are used in re-finding and the difficulties people encounter when using these. We explain how our findings could influence the design of email-clients and outline our thoughts on how future, more in depth analyses, can build on the work presented here to achieve a fuller understanding of email behavior and the support that people need.

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    cover image ACM Conferences
    SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
    July 2011
    1374 pages
    ISBN:9781450307574
    DOI:10.1145/2009916
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 24 July 2011

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    Author Tags

    1. naturalistic user evaluation
    2. personal information management
    3. query log analysis
    4. re-finding

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    • (2022)Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud StorageProceedings of the 35th Annual ACM Symposium on User Interface Software and Technology10.1145/3526113.3545704(1-11)Online publication date: 29-Oct-2022
    • (2022)'It's on the tip of my tongue'Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498421(48-56)Online publication date: 11-Feb-2022
    • (2022)Landscape of Automated Log Analysis: A Systematic Literature Review and Mapping StudyIEEE Access10.1109/ACCESS.2022.315254910(21892-21913)Online publication date: 2022
    • (2020)Personalized Context-aware Collaborative Online Activity PredictionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33698293:4(1-28)Online publication date: 14-Sep-2020
    • (2019)Revisiting Online Personal Search Metrics with the User in MindProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331266(625-634)Online publication date: 18-Jul-2019
    • (2019)Exploring User Behavior in Email Re-Finding TasksThe World Wide Web Conference10.1145/3308558.3313450(1245-1255)Online publication date: 13-May-2019
    • (2019)Characterizing and Predicting Email Deferral BehaviorProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291028(627-635)Online publication date: 30-Jan-2019
    • (2018)Calendar-Aware Proactive Email RecommendationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210001(655-664)Online publication date: 27-Jun-2018
    • (2018)The Lifetime of Email MessagesProceedings of the 2018 Conference on Human Information Interaction & Retrieval10.1145/3176349.3176398(120-129)Online publication date: 1-Mar-2018
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