question: In this work, we apply topic modeling to study what users have been discussing in Twitter during the beginning of the COVID-19 pandemic More particularly, we explore the period of time that includes three differentiated phases of the COVID-19 crisis in Spain: the pre-crisis time, the outbreak, and the beginning of the lockdown To do so, we first collect a large corpus of Spanish tweets and clean them Then, we cluster the tweets into topics using a Latent Dirichlet Allocation model, and define generative and discriminative routes to later extract the most relevant keywords and sentences for each topic Finally, we provide an exhaustive qualitative analysis about how such topics correspond to the situation in Spain at different stages of the crisis © 2021 Sociedad Española para el Procesamiento del Lenguaje Natural