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Extracting multiple layers of social networks through a 7-month survey using a wearable device: a case study from a farming community in Japan

Author

Listed:
  • Masashi Komori

    (Osaka Electro-Communication University)

  • Kosuke Takemura

    (Shiga University)

  • Yukihisa Minoura

    (Bukkyo University)

  • Atsuhiko Uchida

    (Kyoto University)

  • Rino Iida

    (Kyoto University)

  • Aya Seike

    (Kyoto University)

  • Yukiko Uchida

    (Kyoto University)

Abstract

As individuals are susceptible to social influences from those to whom they are connected, structures of social networks have been an important research subject in social sciences. However, quantifying these structures in real life has been comparatively more difficult. One reason is data collection methods—how to assess elusive social contacts (e.g., unintended brief contacts in a coffee room); however, recent studies have overcome this difficulty using wearable devices. Another reason relates to the multi-layered nature of social relations—individuals are often embedded in multiple networks that are overlapping and complicatedly interwoven. A novel method to disentangle such complexity is needed. Here, we propose a new method to detect multiple latent subnetworks behind interpersonal contacts. We collected data of proximities among residents in a Japanese farming community for 7 months using wearable devices which detect other devices nearby via Bluetooth communication. We performed non-negative matrix factorization (NMF) on the proximity log sequences and extracted five latent subnetworks. One of the subnetworks represented social relations regarding farming activities, and another subnetwork captured the patterns of social contacts taking place in a community hall, which played the role of a “hub” of diverse residents within the community. We also found that the eigenvector centrality score in the farming-related network was positively associated with self-reported pro-community attitude, while the centrality score regarding the community hall was associated with increased self-reported health.

Suggested Citation

  • Masashi Komori & Kosuke Takemura & Yukihisa Minoura & Atsuhiko Uchida & Rino Iida & Aya Seike & Yukiko Uchida, 2022. "Extracting multiple layers of social networks through a 7-month survey using a wearable device: a case study from a farming community in Japan," Journal of Computational Social Science, Springer, vol. 5(1), pages 1069-1094, May.
  • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-022-00162-y
    DOI: 10.1007/s42001-022-00162-y
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    References listed on IDEAS

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