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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27350/21:10247971

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos; 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50702 - Urban studies (planning and development)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sustainable Cities and Society

ISSN

2210-6707

e-ISSN

—

Svazek periodika

64

Číslo periodika v rámci svazku

leden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

—

Kód UT WoS článku

000598812600008

EID výsledku v databázi Scopus

2-s2.0-85092171686