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The COVID-19 pandemic that has hit the planet offers a spectacular case study in disaster management. In this literature, the participatory paradigm is fundamental: the mitigation of the impact of the disaster, the quality of the preparation, and the resilience of the society all facilitate reconstruction, but depend on the participation of populations. It remains essential to observe and measure the mental health of populations (anxiety, confidence, hopes, etc.), identifying points of controversy and the content of the discourse, in order to support measures to encourage this participation. Social media, and in particular Twitter, offer valuable resources for exploring this discourse. The main result is based on the identification of the mask’s centrality and aims to establish the importance of this phenomenon. We show this quantitatively, and we explore the concept using NLP methods. The background is a major change in how the crisis is understood. While at the beginning of the cycle it is perceived as something exotic, it later becomes endemic to the social body. We draw on a database of 2.1 million tweets extracted from a corpus of 110 million, put together by an international data science team and dealing with variants of #COVID-19, #coronavirus, etc: the “COVID-19 Twitter data set.”