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

Masashi Komori (), Kosuke Takemura (), Yukihisa Minoura, Atsuhiko Uchida, Rino Iida, Aya Seike and Yukiko Uchida
Additional contact information
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

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 45, 1069-1094

Abstract: 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.

Keywords: Wearable device; Farming community; Social network; Non-negative matrix factorization (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42001-022-00162-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-022-00162-y

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-022-00162-y

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().