Social media semantic perceptions on Madrid Metro system: Using Twitter data to link complaints to space
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10247971" target="_blank" >RIV/61989100:27240/21:10247971 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27350/21:10247971
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2210670720307460?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210670720307460?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.scs.2020.102530" target="_blank" >10.1016/j.scs.2020.102530</a>
Alternative languages
Result language
angličtina
Original language name
Social media semantic perceptions on Madrid Metro system: Using Twitter data to link complaints to space
Original language description
Social 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50702 - Urban studies (planning and development)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sustainable Cities and Society
ISSN
2210-6707
e-ISSN
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Volume of the periodical
64
Issue of the periodical within the volume
leden
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
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UT code for WoS article
000598812600008
EID of the result in the Scopus database
2-s2.0-85092171686