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Social media semantic perceptions on Madrid Metro system: Using Twitter data to link complaints to space

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dc.contributor.authorHorak, Jiri
dc.contributor.authorSvoboda, Radek
dc.contributor.authorGarcía Ruiz, Yolanda
dc.contributor.authorOsorio Arjona, Joaquín
dc.date.accessioned2024-05-20T11:29:50Z
dc.date.available2024-05-20T11:29:50Z
dc.date.issued2021-01-01
dc.description.abstractSocial networks are platforms widely used by travelers who express their opinions about many services like public transport. This paper investigates the value of texts from social networks as a data source for detecting the spatial distribution of problems within a public transit network by geolocating citizens' feelings, and analyzes the effects some factors such as population or income have over that spatial spread, with the goal of developing a more intelligent and sustainable public transit service. For that purpose, Twitter data from the Madrid Metro account is collected over a two-month period. Topics and sentiments are identified from text mining and machine learning algorithms, and mapped to explore spatial and temporal patterns. Lastly, a Geographically Weighted Regression model is used to explore the causality of the spatial distribution of complaining users, by using official data sources as exploratory variables. Results show Twitter users tend to be mid-income workers who reside in peripheral areas and mainly tweet when traveling to workplaces. The main detected problems were punctuality and breakdowns in transfer stations or in central areas, mainly in the early morning of weekdays, and affected by density of points of interest in destination areas.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.1016/j.scs.2020.102530
dc.identifier.issn2210-6707; eISSN: 2210-6715
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12108
dc.journal.titleSustainable Cities and Society
dc.journal.volume64
dc.language.isoen
dc.publisherElsevier
dc.relation.centerFacultad de Geografía e Historia
dc.relation.departmentGeografía
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsTwitter
dc.subject.keywordsPublic transport
dc.subject.keywordsText mining
dc.subject.keywordsSentiment analysis
dc.subject.keywordsGeographically Weighted Regression
dc.titleSocial media semantic perceptions on Madrid Metro system: Using Twitter data to link complaints to spacees
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
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