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Open Access

Embedding Social Perception Dimensions in a Semantic Space: Toward a Quantitative Synthesis

Department of Sociology, The Chinese University of Hong Kong, Hong Kong 999077, China
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Abstract

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.

Keywords

social perception modelword embeddingstext-based analysiscontent dimension

References

[1]

E. T. Higgins and J. A. Bargh, Social cognition and social perception, Annu. Rev. Psychol., vol. 38, pp. 369–425, 1987.
Crossref Google Scholar
[2]

A. E. Abele and B. Wojciszke, Agency and communion from the perspective of self versus others, J. Pers. Soc. Psychol., vol. 93, no. 5, pp. 751–763, 2007.
Crossref Google Scholar
[3]
A. E. Abele and B. Wojciszke, Communal and agentic content in social cognition: A dual perspective model, in Advances in Experimental Social Psychology, M. P. Zanna, ed. Pittsburgh, PA, USA: Academic Press, 2014, pp. 195–255.
Crossref
[4]

A. J. C. Cuddy, S. T. Fiske, and P. Glick, The BIAS map: Behaviors from intergroup affect and stereotypes, J. Pers. Soc. Psychol., vol. 92, no. 4, pp. 631–648, 2007.
Crossref Google Scholar
[5]
A. J. C. Cuddy, S. T. Fiske, and P. Glick, Warmth and competence as universal dimensions of social perception: The stereotype content model and the BIAS map, in Advances in Experimental Social Psychology, M. P. Zanna, ed. Pittsburgh, PA, USA: Academic Press, 2008, pp. 61–149.
Crossref
[6]

N. Kervyn, S. T. Fiske, and V. Y. Yzerbyt, Integrating the stereotype content model (warmth and competence) and the osgood semantic differential (evaluation, potency, and activity), Eur. J. Soc. Psychol., vol. 43, no. 7, pp. 673–681, 2013.
Crossref Google Scholar
[7]
A. Koch, V. Yzerbyt, A. Abele, N. Ellemers, and S. T. Fiske, Social evaluation: Comparing models across interpersonal, intragroup, intergroup, several-group, and many-group contexts, in Advances in Experimental Social Psychology, A. M. Ryan, ed. Pittsburgh, PA, USA: Academic Press, 2021, pp. 1–68.
Crossref
[8]

V. Yzerbyt, Intergroup stereotyping, Curr. Opin. Psychol., vol. 11, pp. 90–95, 2016.
Crossref Google Scholar
[9]

S. T. Fiske, Stereotype content: Warmth and competence endure, Curr. Dir. Psychol. Sci., vol. 27, no. 2, pp. 67–73, 2018.
Crossref Google Scholar
[10]

M. Bertrand and S. Mullainathan, Are emily and greg more employable than lakisha and jamal? A field experiment on labor market discrimination, Am. Econ. Rev., vol. 94, no. 4, pp. 991–1013, 2004.
Crossref Google Scholar
[11]
M. Gilens, “Race coding” and white opposition to welfare, Am. Polit. Sci. Rev., vol. 90, no. 3, pp. 593–604, 1996.
Crossref
[12]

A. J. Nederhof, Methods of coping with social desirability bias: A review, Eur. J. Soc. Psychol., vol. 15, no. 3, pp. 263–280, 1985.
Crossref Google Scholar
[13]
G. Nicolas, X. Bai, and S. T. Fiske, A spontaneous stereotype content model: Taxonomy, properties, and prediction, J. Pers. Soc. Psychol., vol. 123, no. 6, pp. 1243–1263, 2022.
Crossref
[14]
S. T. Fiske, A. J. C. Cuddy, P. Glick, and J. Xu, A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition, J. Pers. Soc. Psychol., vol. 82, no. 6, pp. 878–902, 2002.
Crossref
[15]
C. E. Osgood, G. J. Suci, and P. H. Tannenbaum, The Measurement of Meaning. Urbana, IL, USA: University of Illinois Press, 1957.
[16]

A. E. Abele, N. Ellemers, S. T. Fiske, A. Koch, and V. Yzerbyt, Navigating the social world: Toward an integrated framework for evaluating self, individuals, and groups, Psychol. Rev., vol. 128, no. 2, pp. 290–314, 2021.
Crossref Google Scholar
[17]

G. Nicolas, X. Bai, and S. T. Fiske, Comprehensive stereotype content dictionaries using a semi-automated method, Eur. J. Soc. Psychol., vol. 51, no. 1, pp. 178–196, 2021.
Crossref Google Scholar
[18]

S. T. Fiske, A. J. C. Cuddy, and P. Glick, Universal dimensions of social cognition: Warmth and competence, Trends Cogn. Sci., vol. 11, no. 2, pp. 77–83, 2007.
Crossref Google Scholar
[19]

S. Bruckmüller and A. E. Abele, The density of the big two, Soc. Psychol., vol. 44, no. 2, pp. 63–74, 2013.
Crossref Google Scholar
[20]
Q. Li, C. Wang, and Z. Wang, The dual role of AI: A capable assistant in collaboration and a scapegoat in competition, International Journal of Human–Computer Interaction, doi: 10.1080/10447318.2025.2462755.
Crossref
[21]

F. Durante, S. T. Fiske, N. Kervyn, A. J. C. Cuddy, A. D. Akande, B. E. Adetoun, M. F. Adewuyi, M. M. Tserere, A. A. Ramiah, K. A. Mastor, et al., Nations’ income inequality predicts ambivalence in stereotype content: How societies mind the gap, British Journal of Social Psychology, vol. 52, no. 4, pp. 726–746, 2013.
Crossref Google Scholar
[22]

H. R. Markus and S. Kitayama, Culture and the self: Implications for cognition, emotion, and motivation, Psychol. Rev., vol. 98, no. 2, pp. 224–253, 1991.
Crossref Google Scholar
[23]

K. Phalet and E. Poppe, Competence and morality dimensions of national and ethnic stereotypes: A study in six eastern-European countries, Eur. J. Soc. Psychol., vol. 27, no. 6, pp. 703–723, 1997.
Crossref Google Scholar
[24]

B. Wojciszke, R. Bazinska, and M. Jaworski, On the dominance of moral categories in impression formation, Personality and Social Psychology Bulletin, vol. 24, no. 12, pp. 1251–1263, 1998.
Crossref Google Scholar
[25]

A. E. Abele, The dynamics of masculine-agentic and feminine-communal traits: Findings from a prospective study, J. Pers. Soc. Psychol., vol. 85, no. 4, pp. 768–776, 2003.
Crossref Google Scholar
[26]

J. J. Skowronski and D. E. Carlston, Social judgment and social memory: The role of cue diagnosticity in negativity, positivity, and extremity biases, J. Pers. Soc. Psychol., vol. 52, no. 4, pp. 689–699, 1987.
Crossref Google Scholar
[27]

A. J. C. Cuddy, S. T. Fiske, and P. Glick, When professionals become mothers, warmth doesn’t cut the ice, J. Soc. Issues, vol. 60, no. 4, pp. 701–718, 2004.
Crossref Google Scholar
[28]
N. Kervyn, V. Y. Yzerbyt, C. M. Judd, and A. Nunes, A question of compensation: The social life of the fundamental dimensions of social perception, J. Pers. Soc. Psychol., vol. 96, no. 4, pp. 828–842, 2009.
Crossref
[29]

A. S. Rosette, C. Z. Koval, A. Ma, and R. Livingston, Race matters for women leaders: Intersectional effects on agentic deficiencies and penalties, Leadersh. Q., vol. 27, no. 3, pp. 429–445, 2016.
Crossref Google Scholar
[30]

A. E. Abele, A. J. C. Cuddy, C. M. Judd, and V. Y. Yzerbyt, Fundamental dimensions of social judgment, Eur. J. Soc. Psychol., vol. 38, no. 7, pp. 1063–1065, 2008.
Crossref Google Scholar
[31]
A. Alhothali and J. Hoey, Good news or bad news: Using affect control theory to analyze readers’ reaction towards news articles, in Proc. 2015 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, CO, USA, 2015, pp. 1548–1558.
Crossref
[32]
R. Xiang, Y. Long, M. Wan, J. Gu, Q. Lu, and C. R. Huang, Affection-driven neural networks for sentiment analysis, in Proc. 12th Language Resources and Evaluation Conf., Marseille, France, 2020, pp. 112–119.
[33]

S. M. Mohammad and S. Kiritchenko, Using hashtags to capture fine emotion categories from tweets, Comput. Intell., vol. 31, no. 2, pp. 301–326, 2015.
Crossref Google Scholar
[34]
J. H. Morgan, J. Zhao, N. Zöller, A. Sedlacek, L. Chen, H. Piper, Y. Beck, K. B. Rogers, J. Hoey, and T. Schröder, Modeling the culture of online collaborative groups with affect control theory, in Advances in Social Simulation, P. Ahrweiler and M. Neumann, eds. Cham, Switzerland: Springer International Publishing, 2021, pp. 147–160.
Crossref
[35]

D. R. Heise, Affect control theory: Concepts and model, J. Math. Sociol., vol. 13, nos. 1&2, pp. 1–33, 1987.
Crossref Google Scholar
[36]

A. M. Harris-Watson, L. E. Larson, N. Lauharatanahirun, L. A. DeChurch, and N. S. Contractor, Social perception in human-AI teams: Warmth and competence predict receptivity to AI teammates, Comput. Hum. Behav., vol. 145, p. 107765, 2023.
Crossref Google Scholar
[37]
T. Aktan, Compensatory nature of mixed stereotypes: An investigation of underlying mechanisms in the framework of stereotype content model, PhD dissertation, Department of Psychology, Middle East Technical University, Türkiye, 2012.
[38]
S. Rosenberg, C. Nelson, and P. S. Vivekananthan, A multidimensional approach to the structure of personality impressions, J. Pers. Soc. Psychol., vol. 9, no. 4, pp. 283–294, 1968.
Crossref
[39]

A. J. Cuddy, S. T. Fiske, V. S. Kwan, P. Glick, S. Demoulin, J. P. Leyens, M. H. Bond, J. C. Croizet, N. Ellemers, E. Sleebos, et al., Stereotype content model across cultures: Towards universal similarities and some differences, British Journal of Social Psychology, vol. 48, no. 1, pp. 1–33, 2009.
Crossref Google Scholar
[40]

S. E. Asch, Forming impressions of personality, J. Abnorm. Psychol., vol. 41, pp. 258–290, 1946.
Crossref Google Scholar
[41]

C. M. Judd, T. Garcia-Marques, and V. Y. Yzerbyt, The complexity of relations between dimensions of social perception: Decomposing bivariate associations with crossed random factors, J. Exp. Soc. Psychol., vol. 82, pp. 200–207, 2019.
Crossref Google Scholar
[42]
T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, arXiv preprint arXiv: 1301.3781, 2013.
[43]
J. W. Pennebaker, R. L. Boyd, K. Jordan, and K. Blackburn, The development and psychometric properties of LIWC2015, https://repositories.lib.utexas.edu/items/705e81ca-940d-4c46-94ec-a52ffdc3b51f, 2015.
[44]

A. C. Kozlowski, M. Taddy, and J. A. Evans, The geometry of culture: Analyzing the meanings of class through word embeddings, Am. Sociol. Rev., vol. 84, no. 5, pp. 905–949, 2019.
Crossref Google Scholar
[45]

K. Durrheim, M. Schuld, M. Mafunda, and S. Mazibuko, Using word embeddings to investigate cultural biases, Br. J. Soc. Psychol., vol. 62, no. 1, pp. 617–629, 2023.
Crossref Google Scholar
[46]
X. Qin and T. Tam, Stereotype content dictionary: A semantic space of 3 million words and phrases using Google News Word2Vec embeddings, in Proc. Social, Cultural, and Behavioral Modeling: 16th International Conference, SBP-BRiMS 2023, Pittsburgh, PA, USA, 2023, pp. 12–22.
Crossref
[47]

A. J. Cuddy, E. B. Wolf, P. Glick, S. Crotty, J. Chong, and M. I. Norton, Men as cultural ideals: Cultural values moderate gender stereotype content, J. Pers. Soc. Psychol., vol. 109, no. 4, pp. 622–635, 2015.
Crossref Google Scholar
[48]

T. E. S. Charlesworth, A. Caliskan, and M. R. Banaji, Historical representations of social groups across 200 years of word embeddings from Google Books, Proc. Natl. Acad. Sci. USA, vol. 119, no. 28, p. e2121798119, 2022.
Crossref Google Scholar
[49]

A. Pietraszkiewicz, M. Formanowicz, M. Gustafsson Sendén, R. L. Boyd, S. Sikström, and S. Sczesny, The big two dictionaries: Capturing agency and communion in natural language, Eur. J. Soc. Psychol., vol. 49, no. 5, pp. 871–887, 2019.
Crossref Google Scholar
[50]
B. Mathew, S. Sikdar, F. Lemmerich, and M. Strohmaier, The POLAR framework: Polar opposites enable interpretability of pre-trained word embeddings, in Proc. The Web Conf. 2020, Taipei, China, 2020, pp. 1548–1558.
Crossref
[51]
K. C. Fraser, I. Nejadgholi, and S. Kiritchenko, Understanding and countering stereotypes: A computational approach to the stereotype content model, arXiv preprint arXiv: 2106.02596, 2021.
[52]

K. C. Fraser, S. Kiritchenko, and I. Nejadgholi, Computational modeling of stereotype content in text, Front. Artif. Intell., vol. 5, p. 826207, 2022.
Crossref Google Scholar
[53]

A. van Loon and J. Freese, Word embeddings reveal how fundamental sentiments structure natural language, Am. Behav. Sci., vol. 67, no. 2, pp. 175–200, 2023.
Crossref Google Scholar
[54]
S. Mohammad, B. Dorr, and G. Hirst, Computing word-pair antonymy, in Proc. Conf. Empirical Methods in Natural Language Processing, Honolulu, HI, USA, 2008, pp. 982–991.
Crossref
[55]

S. M. Mohammad, B. J. Dorr, G. Hirst, and P. D. Turney, Computing lexical contrast, Comput. Linguist., vol. 39, no. 3, pp. 555–590, 2013.
Crossref Google Scholar
[56]
Z. Chen, W. Lin, Q. Chen, X. Chen, S. Wei, H. Jiang, and X. Zhu, Revisiting word embedding for contrasting meaning, in Proc. 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Int. Joint Conf. Natural Language Processing (Volume 1: Long Papers), Beijing, China, 2015, pp. 106–115.
Crossref
[57]

P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, Enriching word vectors with subword information, Trans. Assoc. Comput. Linguist., vol. 5, pp. 135–146, 2017.
Crossref Google Scholar
[58]
J. Pennington, R. Socher, and C. Manning, Glove: Global vectors for word representation, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014, pp. 1532–1543.
Crossref
[59]
A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, Bag of tricks for efficient text classification, arXiv preprint arXiv: 1607.01759, 2016.
Crossref
[60]
T. Kenter and M. de Rijke, Short text similarity with word embeddings, in Proc. 24th ACM Int. on Conf. Information and Knowledge Management, Melbourne, Australia, 2015, pp. 1411–1420.
Crossref
[61]

G. A. Miller, WordNet, Commun. ACM, vol. 38, no. 11, pp. 39–41, 1995.
Crossref Google Scholar
[62]
C. E. Osgood, W. H. May, and M. S. Miron, Cross-Cultural Universals of Affective Meaning. Urbana, IL, USA: University of Illinois Press, 1975.
[63]
J. S. Wiggins, A psychological taxonomy of trait-descriptive terms: The interpersonal domain, J. Pers. Soc. Psychol., vol. 37, no. 3, pp. 395–412, 1979.
Crossref
[64]

M. Formanowicz, A. Goldenberg, T. Saguy, A. Pietraszkiewicz, M. Walker, and J. J. Gross, Understanding dehumanization: The role of agency and communion, J. Exp. Soc. Psychol., vol. 77, pp. 102–116, 2018.
Crossref Google Scholar
[65]

M. J. Hornsey, Social identity theory and self-categorization theory: A historical review, Soc. Pers. Psychol. Compass, vol. 2, no. 1, pp. 204–222, 2008.
Crossref Google Scholar
[66]
V. Yzerbyt and O. Corneille, Cognitive process: Reality constraints and integrity concerns in social perception, in On the Nature of Prejudice: Fifty Years after Allport, J. F. Dovidio, P. Glick, and L. A. Rudman, eds. Oxford, UK: Blackwell Publishing, 2005, pp. 175–191.
Crossref
[67]

V. Yzerbyt and L. Cambon, The dynamics of compensation: When ingroup favoritism paves the way for outgroup praise, Pers. Soc. Psychol. Bull., vol. 43, no. 5, pp. 587–600, 2017.
Crossref Google Scholar
[68]

D. R. Heise, The semantic differential and attitude research, Attitude Measurement, vol. 4, pp. 235–253, 1970.
Google Scholar
[69]
O. Levy, Y. Goldberg, and I. Dagan, Improving distributional similarity with lessons learned from word embeddings, Trans. Assoc. Comput. Linguist., vol. 3, pp. 211–225, 2015.
Crossref
[70]
S. N. Chakrabartty, Cosine similarity approaches to reliability of Likert scale and items, Romanian Journal of Psychological Studies, vol. 6, no. 1, pp. 3–16, 2018.
[71]
L. T. Hu and P. M. Bentler, Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Struct. Equ. Model. A Multidiscip. J., vol. 6, no. 1, pp. 1–55, 1999.
Crossref
[72]
H. W. Marsh, K. T. Hau, and Z. Wen, In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing hu and bentler’s (1999) findings, Struct. Equ. Model. A Multidiscip. J., vol. 11, no. 3, pp. 320–341, 2004.
Crossref
Journal of Social Computing
Volume 6 Issue 2,
June 2025
Pages 95-111
DOI: 10.23919/JSC.2025.0010
Cite this article:
Qin X, Tam T. Embedding Social Perception Dimensions in a Semantic Space: Toward a Quantitative Synthesis. Journal of Social Computing, 2025, 6(2): 95-111. https://doi.org/10.23919/JSC.2025.0010

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Received: 04 April 2025
Revised: 31 May 2025
Accepted: 01 June 2025
Published: 30 June 2025
© The author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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