Cultural Cartography with Word Embeddings
arXiv:2007.04508 · doi:10.1016/j.poetic.2021.101567
Abstract
Using the frequency of keywords is a classic approach in the formal analysis of text, but has the drawback of glossing over the relationality of word meanings. Word embedding models overcome this problem by constructing a standardized and continuous "meaning space" where words are assigned a location based on relations of similarity to other words based on how they are used in natural language samples. We show how word embeddings are commensurate with prevailing theories of meaning in sociology and can be put to the task of interpretation via two kinds of navigation. First, one can hold terms constant and measure how the embedding space moves around them--much like astronomers measured the changing of celestial bodies with the seasons. Second, one can also hold the embedding space constant and see how documents or authors move relative to it--just as ships use the stars on a given night to determine their location. Using the empirical case of immigration discourse in the United States, we demonstrate the merits of these two broad strategies for advancing important topics in cultural theory, including social marking, media fields, echo chambers, and cultural diffusion and change more broadly.
References in corpus (24)
- Distributed Representations of Words and Phrases and their Compositionality
- Semantics derived automatically from language corpora contain human-like biases
- The Symbol Grounding Problem
- Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
- The Diversity-Innovation Paradox in Science
- Autoencoding beyond pixels using a learned similarity metric
- The Geometry of Culture: Analyzing Meaning through Word Embeddings
- A Survey Of Cross-lingual Word Embedding Models
- Distributional Semantics and Linguistic Theory
- On the Dimensionality of Word Embedding
- Survey of Computational Approaches to Lexical Semantic Change
- Quantifying and Reducing Stereotypes in Word Embeddings
- "Go eat a bat, Chang!": On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19
- Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings
- Political Discussions in Homogeneous and Cross-Cutting Communication Spaces
- Contextual Word Representations: A Contextual Introduction
- Multiplex Communities and the Emergence of International Conflict
- What can you do with a rock? Affordance extraction via word embeddings
- Word Mover's Embedding: From Word2Vec to Document Embedding
- Speeding up Word Mover's Distance and its variants via properties of distances between embeddings
- A Quantitative Approach to Understanding Online Antisemitism
- An Efficient Shared-memory Parallel Sinkhorn-Knopp Algorithm to Compute the Word Mover's Distance
- Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat
- Interpretable Word Embeddings via Informative Priors