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To extract essential information from complex data, computer scientists havebeen developing machine learning models that learn low-dimensionalrepresentation mode. From such advances in machine learning research, not onlycomputer scientists but also social scientists have benefited and advancedtheir research because human behavior or social phenomena lies in complex data.To document this emerging trend, we survey the recent studies that apply wordembedding techniques to human behavior mining, building a taxonomy toillustrate the methods and procedures used in the surveyed papers and highlightthe recent emerging trends applying word embedding models to non-textual humanbehavior data. This survey conducts a simple experiment to warn that commonsimilarity measurements used in the literature could yield different resultseven if they return consistent results at an aggregate level.

Word Embedding for Social Sciences: An Interdisciplinary Survey