A Scientometric Analysis of Self-tracking in Relation to Artificial Intelligence and Big Data

by | Sep 16, 2020 | Research Publications

Author(s):

  • Xue Wu
  • Man Zhao
  • Han-Teng Liao
  • Si-Pan Sun

Abstract:

As an increasing amount of personal data has been gathered by wearable and mobile devices, self-tracking, or the practice that people keep track, has become an important topic in Artificial Intelligence (AI) and big data applications. With the aim to provide a systematic review of the literature on self-tracking, this paper presents a scientometric analysis of 109 articles since 2000 collected from the Web of Science. Based on keyword co-occurrence network analysis, the paper has identified four major clusters: (1) wearables as quantified-self applications; (2) big data and critical theory; (3) data and privacy; (4) personal informatics. The further keywords-in-context (KWIC) analysis of the abstracts of the dataset clarifies the seemingly-interchangeable notions of “self-tracking” and “quantified-self”: While “self-tracking” refers to more general activities, practices, technologies, and applications of keeping tracks, “quantified-self” refers to the more conscious efforts and meaning-making outcomes of the self-tracking activities. Such clarification, along with the keyword network analysis, suggests that self-tracking has become a specific and major type of datafication of human conditions or existence and that quantified-self is the construction of self through such datafication. A more integrated conceptual framework is needed for future research to better understand what amounts to meaningful datafication of human conditions and existence, thereby helping researchers and designers to discern the classic notions of health, wellness, and happiness for better research and design outcomes.

Documentation:

https://doi.org/10.1088/1757-899X/806/1/012014

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