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Ways of Knowing in HCI pp 349–372Cite as

Understanding User Behavior Through Log Data and Analysis

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Abstract

HCI researchers are increasingly collecting rich behavioral traces of user interactions with online systems in situ at a scale not previously possible. These logs can be used to characterize user interactions with existing systems and compare different designs. Large-scale log studies give rise to new challenges in experimental design, data collection and interpretation, and ethics. The chapter discusses how to address these challenges using search engine logs, but the methods are applicable to other types of log data.

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Author information

Authors and Affiliations

  1. Microsoft Research One Microsoft Way, Redmond, WA, 98005, USA

    Susan Dumais & Jaime Teevan

  2. Google, Inc., 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA

    Robin Jeffries, Daniel M. Russell & Diane Tang

Authors
  1. Susan Dumais
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  2. Robin Jeffries
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  3. Daniel M. Russell
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  4. Diane Tang
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  5. Jaime Teevan
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Corresponding author

Correspondence to Susan Dumais .

Editor information

Editors and Affiliations

  1. University of California, Irvine, Irvine, California, USA

    Judith S. Olson

  2. Group Social Computing, IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Wendy A. Kellogg

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Dumais, S., Jeffries, R., Russell, D.M., Tang, D., Teevan, J. (2014). Understanding User Behavior Through Log Data and Analysis. In: Olson, J., Kellogg, W. (eds) Ways of Knowing in HCI. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0378-8_14

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  • DOI: https://doi.org/10.1007/978-1-4939-0378-8_14

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-0377-1

  • Online ISBN: 978-1-4939-0378-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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