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Web behavior analysis in social life logging

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Abstract

Collection of web behavior relies on self-report and web logs in daily life. The self-report is derived on memory and can seem ambiguous for analyzing web behavior. It is necessary to quantitatively measure these web behaviors to see the changes over time. Individuals tied by the same emotion become a group, and its members synchronize with each other. Therefore, this study is to propose a web behavior model and its collecting and analyzing methods. Additionally, this study is to create a group using the synchronization of behavior patterns and social networking to provide insight into enhanced marketing.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. NRF-2020R1A2B5B02002770).

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Authors and Affiliations

  1. Team of Technology Development, Emotion Science Center, Jahamun-ro 6-gil, Jongno-gu, Seoul, 03044, Republic of Korea

    Youngho Jo

  2. Department of Emotion Engineering, Sangmyung University, Hongji-dong, Jongno-gu, Seoul, 03016, Republic of Korea

    Hyunwoo Lee & Ayoung Cho

  3. Department of Human-Centered Artficial Intelligence, Sangmyung University, Hongji-dong, Jongno-gu, Seoul, 03016, Republic of Korea

    Mincheol Whang

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  1. Youngho Jo
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  2. Hyunwoo Lee
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  3. Ayoung Cho
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  4. Mincheol Whang
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Correspondence to Mincheol Whang.

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Jo, Y., Lee, H., Cho, A. et al. Web behavior analysis in social life logging. J Supercomput 77, 1301–1320 (2021). https://doi.org/10.1007/s11227-020-03304-z

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