Social evaluation is fundamental to everyday interactions, yet our understanding has been constrained by fragmented theories and the lack of a scalable method for tracking group attitudes in real time. This paper resolves this methodological gap by introducing and validating a computational framework that empirically synthesizes three major theoretical models (Stereotype Content Model, Dual Perspective Model, and Semantic Differential) within a unified word embedding space. We demonstrate that social evaluation is structured by two core latent dimensions: Warmth-Communion-Evaluation (WCE), capturing affective and moral judgments, and Competence-Agency (CA), reflecting perceptions of ability and effectiveness. To validate its real-world utility, we apply this framework to U.S.-based Twitter posts about Chinese and Japanese individuals before and during the COVID-19 pandemic. Our analysis reveals that while perceptions of competence (CA) remained stable, affective evaluations (WCE) of Chinese individuals declined sharply, a dynamic not observed for Japanese individuals. This work offers a robust, scalable instrument for tracking intergroup attitudes during crises and provides a crucial bridge between social psychological theory and computational social science, enabling the real-time analysis of intergroup dynamics.
Qin et al. (Sat,) studied this question.