Evolving Contextual Semantics

Similar to biological systems, natural languages are evolving systems with words as their measurable units. Words have certain functions within a body of text to convey ideas and thought. This poster presentation introduces the method of Latent Semantic Analysis (LSA) and the Skip-Gram with Negative Sampling (SGNS) approaches to extract contextual semantics within a body of text taken from a social media platform called Twitter. Contextual semantics refers to a semantic space that is expressed as a linear combination of words from a matrix subspace. Due to the natural volatility of some words and languages as a whole, the semantic space is evolving in time. The objective is to study the emergence of online social movements particularly the use of hashtags. We explore the evolving contextual semantics of the social movement hashtags “#blacklivesmatter” and “#metoo” as examples. From our results and observations, we hypothesize that these hashtags exhibit selective language transmission (i.e. horizontal transmission), the process of passing words and phrases between people that lead to changes in meanings due to selective cultural pressures.