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3 results

  • Biases about Chinese People in English Language Use: Stereotypes, Prejudice and Discrimination

  • Han-Wu-Shuang Bao, Peter Gries
  • Journal:
    The China Quarterly , First View
    Published online by Cambridge University Press:
    18 June 2025, pp. 1-13
    • Article
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  • StopAsianHate protests arose in the West during the COVID-19 pandemic, opposing a perceived increase in hate incidents directed against Asians in general and Chinese people in particular. These events raise a question: what is the nature of attitudinal biases about Chinese people in the English-speaking world today? Here, we seek answers with AI and big data. Using BERT language models pre-trained on massive English-language corpora (books, news articles, Wikipedia, Reddit and Twitter) and a new method for measuring natural-language propositions (the Fill-Mask Association Test, FMAT), we examined three components of attitudinal biases about Chinese people: stereotypes (cognitive beliefs), prejudice (emotional feelings) and discrimination (behavioural tendencies). The FMAT uncovered relative semantic associations between Chinese people and (1) cognitive stereotypes of low warmth (less moral/trustworthy and less sociable/friendly) and somewhat low competence (less assertive/dominant but equally capable/intelligent); (2) affective prejudice of contempt (vs admiration); and (3) behavioural discrimination of active/passive harm (vs help/cooperation). These findings advance our understanding of attitudinal biases towards Chinese people in the English-speaking world.

  • 18 - Social Computing: Answer vs. Vote

  • from Part III - Sequential Decision-Making
  • Yan Chen, University of Science and Technology of China, Chih-Yu Wang, Chunxiao Jiang, Tsinghua University, Beijing, K. J. Ray Liu, University of Maryland, College Park
  • Book:
    Reciprocity, Evolution, and Decision Games in Network and Data Science
    Published online:
    01 July 2021
    Print publication:
    22 July 2021, pp 423-451

  • Summary

    In many social computing systems, users decide sequentially whether to participate or not and, if they participate, whether to create a piece of content directly (i.e. answering) or to rate existing content contributed by previous users (i.e. voting). We present in this chapter a game-theoretic model that formulates the sequential decision-making of strategic users under the presence of this answering–voting externality. We prove theoretically the existence and uniqueness of a pure strategy equilibrium. We show that there exist advantages for users with higher abilities and for answering earlier. Therefore, the equilibrium exhibits a threshold structure and the threshold for answering gradually increases as answers accumulate. To show the validness of the game-theoretic model, we analyze user behavior data collected from a popular question-and-answer site Stack Overflow and show that the main qualitative predictions of the game-theoretic model match up with observations made from the data. Finally, we formulate the system designer’s problem and abstract several design principles that could potentially guide the design of incentive mechanisms for social computing systems in practice.

Modern Dimension Reduction

Modern Dimension Reduction
  • Philip D. Waggoner
  • Published online:
    10 July 2021
    Print publication:
    05 August 2021
  • Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.