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From Videos to URLs: A Multi-Browser Guide to Extract User’s Behavior with Optical Character Recognition

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Advances in Computer Vision (CVC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 943))

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Abstract

Tracking users’ activities on the World Wide Web (WWW) allows researchers to analyze each user’s internet behavior as time passes and for the amount of time spent on a particular domain. This analysis can be used in research design, as researchers may access to their participant’s behaviors while browsing the web. Web search behavior has been a subject of interest because of its real-world applications in marketing, digital advertisement, and identifying potential threats online. In this paper, we present an image-processing based method to extract domains which are visited by a participant over multiple browsers during a lab session. This method could provide another way to collect users’ activities during an online session given that the session recorder collected the data. The method can also be used to collect the textual content of web-pages that an individual visits for later analysis.

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Author information

Authors and Affiliations

  1. Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA

    Mojtaba Heidarysafa, Kamran Kowsari & Donald E. Brown

  2. Institute of Law, Psychiatry, and Public Policy, University of Virginia, Charlottesville, VA, USA

    James Reed, April Celeste R. Leviton & Janet I. Warren

  3. Data Science Institute, University of Virginia, Charlottesville, VA, USA

    Donald E. Brown

  4. Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, VA, USA

    Janet I. Warren

  5. Department of Sociology, University of California, Riverside, CA, USA

    April Celeste R. Leviton

Authors
  1. Mojtaba Heidarysafa
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  2. James Reed
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  3. Kamran Kowsari
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  4. April Celeste R. Leviton
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  5. Janet I. Warren
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  6. Donald E. Brown
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Corresponding author

Correspondence to Mojtaba Heidarysafa .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Heidarysafa, M., Reed, J., Kowsari, K., Leviton, A.C.R., Warren, J.I., Brown, D.E. (2020). From Videos to URLs: A Multi-Browser Guide to Extract User’s Behavior with Optical Character Recognition. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_37

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