Word Embedding for Social Sciences: An Interdisciplinary Survey
This survey helps social science researchers integrate word embedding methods, but it is incremental as it primarily organizes existing literature.
The authors surveyed interdisciplinary applications of word embedding techniques in social sciences to address fragmented knowledge, and conducted an experiment showing that common similarity measurements can yield different results despite aggregate consistency.
To extract essential information from complex data, computer scientists have been developing machine learning models that learn low-dimensional representation mode. From such advances in machine learning research, not only computer scientists but also social scientists have benefited and advanced their research because human behavior or social phenomena lies in complex data. However, this emerging trend is not well documented because different social science fields rarely cover each other's work, resulting in fragmented knowledge in the literature. To document this emerging trend, we survey recent studies that apply word embedding techniques to human behavior mining. We built a taxonomy to illustrate the methods and procedures used in the surveyed papers, aiding social science researchers in contextualizing their research within the literature on word embedding applications. This survey also conducts a simple experiment to warn that common similarity measurements used in the literature could yield different results even if they return consistent results at an aggregate level.