Sleeping Lion or Sick Man? Machine Learning Approaches to Deciphering Heterogeneous Images of Chinese in North America

A manifesto for computational sociology: The Canadian perspective

“…Fu et al. (2022) investigated media sentiments related to Chinese in North America since the 1970s, identified distinct topic‐specific temporal trajectories, and suggested a dual process of social and discursive formation of realities.…”

“…To better capture the semantic representations, they replace conventional word embedding models with a transformer model: DistilBERT (Sanh et al., 2019). DistilBERT is adapted from the original Bidirectional Encoder Representations from Transformers (BERT) model (Devlin et al., 2018), which is widely viewed as a milestone in natural language processing and has clear advantages over previous methods in terms of handling long sequences, such as newspaper articles (Fu et al., 2022). They also use Bayesian mixture modules to decipher a latent semantic space, investigate clusters, and interpret heterogeneous meanings.…”