During the COVID-19 pandemic, countries all over the world have tried to prevent the spread of the virus with measures like social distancing, movement limitation, closure of premises and shops, voluntary isolation, lockdown, and curfew. Likely, these limitations have influenced the way people moved within urban spaces. In this study, we use Twitter as a passive sensor to understand how these measures affected human mobility. We focus on the city of Milan, one of the most international and active cities in Italy, but also one of the cities most affected by the spread of the virus. We analyzed more than one million of GPS geo-tagged tweets, posted from 2019 to 2022, and results show that the pandemic has affected human mobility (in 2022, less mobility during work hours and more mobility during the evening hours), and show that social and fashion-related activities are the main reasons people move within the city. This study shows the benefits of using Twitter as a passive sensor to measure human mobility: real-time analysis (not possible with interviews and/or questionnaire) and insights of the reasons behind human mobility (not possible to get with the sole use of telephone operators data).
Twitter as Passive Sensor to Understand How COVID-19 Pandemic Affected Human Mobility / Furini, M.; Montangero, M.. - 2023-:(2023), pp. 213-217. (Intervento presentato al convegno 20th IEEE Consumer Communications and Networking Conference, CCNC 2023 tenutosi a usa nel 2023) [10.1109/CCNC51644.2023.10060726].