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Simple dynamic word embeddings for mapping perceptions in the public sphere

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Figure

Figure 1: Our dynamic embedding model learns an at-tribute invariant embedding for each training wordare summed to computeand averaged across words before classification
Figure 2: Scores produced by (Garg et al., 2018) andour model (blue dotted and green dashed lines, respec-tively) compared to actual workforce participation rates(solid lines) for gender (top) and Asian/White (bottom)linguistic biases
Figure 3: Bias towards refugees as outsiders across talk radio shows from mid-August to mid-September 2018: (a)depicts bias scores computed using a “non-dynamic model”, i.e., training multiple Word2Vec models (one per dayof data) and then projecting these

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