Sánchez Ávila, MarioMouriño García, Marcos AntonioArias Fisteus, JesúsSánchez Fernández, Luis2021-05-182021-05-182020-09-09IEEE Access, vol. 8, Sept. 2020, Pp. 168429-1684382169-3536https://hdl.handle.net/10016/32667We present a system that analyzes data extracted from the microbloging site Twitter to detect the occurrence of events and obstacles that can affect pedestrian mobility, with a special focus on people with impaired mobility. First, the system extracts tweets that match certain prede ned terms. Then, it obtains location information from them by using the location provided by Twitter when available, as well as searching the text of the tweet for locations. Finally, it applies natural language processing techniques to con rm that an actual event that affects mobility is reported and extract its properties (which urban element is affected and how). We also present some empirical results that validate the feasibility of our approach.10engThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.Atribución-NoComercial-SinDerivadas 3.0 EspañaMobility barrierrsSmartcitySocial sensingTransportresearch articleTelecomunicacioneshttps://doi.org/10.1109/ACCESS.2020.3022834open access168429168438IEEE Access8AR/0000027508