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