Embedding Social Perception Dimensions in a Semantic Space: Toward a Quantitative Synthesis
Social perception refers to how individuals interpret and understand the social world. It is a foundational area of theory and measurement within the social sciences, particularly in communication, political science, psychology, and sociology. Classical models include the Stereotype Content Model (S...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2025-06-01
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| Series: | Journal of Social Computing |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/JSC.2025.0010 |
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| Summary: | Social perception refers to how individuals interpret and understand the social world. It is a foundational area of theory and measurement within the social sciences, particularly in communication, political science, psychology, and sociology. Classical models include the Stereotype Content Model (SCM), Dual Perspective Model (DPM), and Semantic Differential (SD). Extensive research has been conducted on these models. However, their interrelationships are still difficult to define using conventional comparison methods, which often lack efficiency, validity, and scalability. To tackle this challenge, we employ a text-based computational approach to quantitatively represent each theoretical dimension of the models. Specifically, we map key content dimensions into a shared semantic space using word embeddings and automate the selection of over 500 contrasting word pairs based on semantic differential theory. The results suggest that social perception can be organized around two fundamental components: subjective evaluation (e.g., how good or likable someone is) and objective attributes (e.g., power or competence). Furthermore, we validate this computational approach with the widely used Rosenberg’s 64 personality traits, demonstrating improvements in predictive performance over previous methods, with increases of 19%, 13%, and 4% for the SD, DPM, and SCM dimensions, respectively. By enabling scalable and interpretable comparisons across these models, our findings would facilitate both theoretical integration and practical applications. |
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| ISSN: | 2688-5255 |