This is a three-part project concerning methods for the study of political semantics ---how the “meaning” of political concepts is represented and organized in memory and implications for political attitudes and behavior. The first chapter proposes a framework for the estimation of group differences in memory representations of political concepts and applies it to evaluate partisan representational differences in the U.S. The second chapter proposes a memory-centered approach to the study of ideology along with the requisite methods for its implementation. The third chapter centers on word embeddings, a deep learning method to estimate word representations from large collections of text. Along with a conceptual overview, it provides practitioners with a series of tests to perform model comparison and validation, including a novel Turing-style test.
|Commitee:||Landa, Dimitri, Larson, Jennifer|
|School:||New York University|
|School Location:||United States -- New York|
|Source:||DAI-A 81/2(E), Dissertation Abstracts International|
|Keywords:||Attitudes, Ideology, Meaning, Polarization, Semantic memory, Word embeddings|
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