Abstract
This study used statistical and social network analysis to investigate on the characteristics and communication patterns at an online community about quantified self. Quantified self refers to self knowledge generated through numbers presented to users via tracking technology, including biological, physical, behavioral information relevant to themselves. Through these quantified self practices, individuals perceived a more sensible, calculatable, and manageable self. Quantified self participants share and discuss their self data and exchange knowledge online, constructing new communities which are connected by numbers. These communities care more about the scientific and professional analysis of the shared self data. Additionally, the self data are more sensitive since they are usually regarded as personal privacy. Therefore, this study aims to explore the uniqueness of these communities’ social network. “Quantified Self” is an online community encouraging its users to share their practices, opinions, and knowledge about self-tracking and health. “Quantified Self” has 5731users and 2193 threads till 21th April 2022. Employing statistical and social network analysis methods, two different communication networks, posting network and reading network, are designed to better analyze the communication patterns. Centrality, Closeness, betweenness and coreness are calculated in both networks, while post degree and read degree are calculated separately. The results suggest that the communication network is dominated by professionals, while the reading network is more decentralized with higher level of diversity of more kinds of active users. Although threads can last for a long-time discussion, the users are not suggested to participate consistently. Some typical cases are discussed.
Presenters
Bingyu ChenStudent, Doctoral Student, Nanyang Technological University, Central Singapore, Singapore Vivian Hsueh Hua Chen
Associate professor , Wee Kim Wee School of Communication and Information, Nanyang Technological University , Singapore
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
SOCIAL MEDIA, INTERNET, INFORMATICS, SOCIAL NETWORK ANALYSIS, ONLINE COMMUNITY
