A Domain Expertise and Word-Embedding Geometric Projection Based Semantic Mining Framework for Measuring the Soft Power of Social Entities

Abstract

Social science research (communication studies in particular) often involves measuring the soft power of social entities from the related big data of media texts. This work proposes a soft power measurement framework that integrates the domain-expertise-driven conceptualization of the traditional social science paradigm and the data-driven operationalization of the computational social science paradigm. Based on the geometric projections of word vectors from the social entity keywords to the soft power dimension keywords, the weights of social entity keywords in the full-sample media texts are projected to the corresponding soft power dimensions in the high-dimensional word-embedding semantic space and accumulated to the total and dimensional values of the soft power of social entities respectively. We demonstrate the applicability, compatibility, computational complexity, validity, adaptability, and scalability of the proposed framework by showing a standard instantiation (measuring the national image carrying capacity of Chinese elite private entrepreneurs from the full-sample WeChat articles, each of which has more than 100,000 unique views) and a variational instantiation (measuring the city brand influence of global cities from the big data of the Global News Knowledge Graph in the Global Database of Events, Language, and Tone (GDELT) of Google) of the proposed framework.