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Word embedding for social sciences: an interdisciplinary survey.

Akira Matsui1, Emilio Ferrara2

  • 1College of Business Administration, Yokohama National University, Yokohama, Kanagawa, Japan.

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Summary
This summary is machine-generated.

This study surveys word embedding applications in social science for human behavior mining. It offers a taxonomy to guide researchers navigating this intersection of computer science and social science.

Keywords:
Bias in machine learningComputational social scienceWord embeddingword2vec

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Area of Science:

  • Computer Science
  • Social Science
  • Computational Social Science

Background:

  • Machine learning models excel at extracting low-dimensional representations from high-dimensional data.
  • Word embedding, a popular machine learning technique, is increasingly applied across various social science disciplines.
  • A documented overview of word embedding's application in social science research is lacking due to its interdisciplinary nature.

Purpose of the Study:

  • To compile fragmented knowledge on word embedding applications in social science.
  • To survey recent studies utilizing word embedding models for human behavior mining.
  • To provide a structured overview of this emerging trend at the intersection of computer science and social science.

Main Methods:

  • Systematic survey of recent academic literature.
  • Analysis of studies applying word embedding models to human behavior mining.
  • Development of a taxonomy to categorize and synthesize findings.

Main Results:

  • Identification of word embedding as a key tool in computational social science.
  • Compilation of diverse applications of word embedding in understanding human behavior.
  • Establishment of a framework for future research in this interdisciplinary area.

Conclusions:

  • Word embedding represents a significant and growing trend in social science research.
  • The developed taxonomy offers a valuable resource for researchers exploring human behavior mining.
  • This work bridges computer science and social science, facilitating further interdisciplinary collaboration.