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This study extends a model for understanding big data by incorporating social psychology and neuroscience insights. It proposes a method using linguistic tools to link individual behavior to big data streams.

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Area of Science:

  • Social Sciences
  • Computational Social Science
  • Neuroscience

Background:

  • The "big data" paradigm offers vast information but lacks social scientific context.
  • Bentley et al. proposed a four-quadrant model for contextualizing big data.
  • Existing models may not fully capture the nuances of human decision-making within big data.

Purpose of the Study:

  • To extend the "big data" contextualization model proposed by Bentley et al.
  • To integrate findings from social psychology and neuroscience into decision-making dimensions.
  • To propose a novel method for connecting individual-level insights with big data.

Main Methods:

  • The study suggests extending the east-west dimension (socially vs. independently motivated decision-making).
  • It incorporates findings from social psychology and neuroscience to refine this dimension.
  • A method leveraging linguistic tools is outlined to bridge disciplinary insights.

Main Results:

  • The proposed extensions enrich the understanding of decision-making drivers in big data contexts.
  • Linguistic tools can effectively connect micro-level (individual) and macro-level (big data) phenomena.
  • This interdisciplinary approach offers a more holistic view of big data.

Conclusions:

  • Contextualizing big data through social science, psychology, and neuroscience is crucial.
  • The proposed model and methods enhance the interpretation of big data by considering individual motivations.
  • Interdisciplinary research is vital for advancing the study of big data and human behavior.