Monitoring the Semantic Change of COVID-19-related Expressions Using Dynamic Word Embeddings

In this paper, we investigate how the COVID-19 pandemic has affected the use of language in the online space through measuring the semantic changes of words during the time that includes the outbreak of the pandemic and the months of the lockdown. As a first step, we apply a recent word embedding technique on a time-labelled text corpus collected from social media which represents the semantic relation of words based on their likelihood of co-occurring next to each other. By analyzing different statistical features of the received dynamic embedding, we can identify and quantitatively describe periods where the semantic properties of a chosen word are undergoing significant changes. Since this depends on the context and the usage of these words by the users, we can infer their reaction to the COVID-19-related events and relevant news dated to these periods.