A new methodology to understand how a word's use and meaning varies over circumstances for political science research
"Political scientists commonly seek to make statements about how a word’s use and meaning varies over circumstances—whether that be time, partisan identity, or some other document-level covariate. A promising avenue is the use of domain-specific word embeddings, that simultaneously allow for statements of uncertainty and statistical inference."
In this session of the CIVICA Data Science Seminar Series, Prof. Arthur Spirling, Professor of Politics and Data Science at New York University will introduce the a la Carte on Text (ConText) embedding regression model for this purpose and evaluate how this method outperforms well-known competitors for studying changes in meaning of words across groups and time.
