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.
@article{arxiv.2102.07836,
title = {How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings},
author = {Yanzhu Guo and Christos Xypolopoulos and Michalis Vazirgiannis},
journal= {arXiv preprint arXiv:2102.07836},
year = {2021}
}