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Words are malleable objects, influenced by events that are reflected inwritten texts. Situated in the global outbreak of COVID-19, our research aimsat detecting semantic shifts in social media language triggered by the healthcrisis. With COVID-19 related big data extracted from Twitter, we trainseparate word embedding models for different time periods after the outbreak.We employ an alignment-based approach to compare these embeddings with ageneral-purpose Twitter embedding unrelated to COVID-19. We also compare ourtrained embeddings among them to observe diachronic evolution. Carrying outcase studies on a set of words chosen by topic detection, we verify that ouralignment approach is valid. Finally, we quantify the size of global semanticshift by a stability measure based on back-and-forth rotational alignment.

How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings