Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang
Words are not static in their usage and meaning, but evolve over time. An interesting phenomenon in languages is slang, which is an informal language that is considered ephemeral and is often associated with contemporary trends. In this work, we study the semantic change and relative frequency shift of slang words and compare this change with standard, nonslang words. To measure semantic change, we obtain contextualized representations of words, reduce their dimensionality and propose a metric to measure their average pairwise distances between two time periods. We apply causal discovery algorithms and causal inference to uncover the dynamics of language evolution and measure the effect that word type (slang/nonslang) has on both semantic change and frequency shift, as well as its relationship to absolute frequency and polysemy. Our causal analysis shows that slang words undergo less semantic change even though they have larger frequency shifts over time.
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