Covid-19 pandemic is deeply affecting urban mobility: the social-distancing strategies adopted to cope with the virus transmission pushed the majority of city users into avoiding public transport services in favour of safe and contactless travel options. To investigate this phenomenon, this paper proposes an Urban Informatics approach to understand passengers’ opinions and expressed polarity through user-generated social-media data (Twitter) in the Greater London Area. The analysed corpus consists of 27,700 tweets posted between January 13rd, 2020 and May 17th, 2021, and geolocated in immediate proximity of a short sample of selected Tube Stations. Data was segmented in several phases, based on the restriction measures enforced by the UK National Authorities. Each subset is then analysed through texts structuring and semantic analyses, with LDA topic modelling and Sentiment Analysis. Finally, the outcomes of these processes are interpreted in their chronological succession and compared to demand data and service-disruption data.