Contribution of geotagged Twitter data in the study of a social group’s activity space

Cebeillac, Alexandre; Rault, Yves-Marie

doi:10.4000/netcom.2529

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Twitter posts, or tweets, are limited to 280 characters. Twitter had 316 million active users globally with 500 million tweets per day in 2016 [64]- [66], and had further increased to 336 million active users in 2018 [67]. There are three primary sources of location information in tweets, the user's home location mentioned in their profile, location provided in the tweet metadata, and location mentioned in the tweet content [26], [51].…”

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Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Das

Purves

2020

IEEE Trans. Intell. Transport. Syst.

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Detecting traffic events and their locations is important for an effective transportation management system and better urban policy making. Traffic events are related to traffic accidents, congestion, parking issues, to name a few. Currently, traffic events are detected through static sensors e.g., CCTV camera, loop detectors. However they have limited spatial coverage and high maintenance cost, especially in developing regions. On the other hand, with Web 2.0 and ubiquitous mobile platforms, people can act as social sensors sharing different traffic events along with their locations. We investigated whether Twitter-a social media platform can be useful to understand urban traffic events from tweets in India. However, such tweets are informal and noisy and containing vernacular geographical information making the location retrieval task challenging. So far most authors have used geotagged tweets to identify traffic events which accounted for only 0.1%-3% or sometimes less than that. Recently Twitter has removed precise geotagging, further decreasing the utility of such approaches. To address these issues, this research explored how ungeotagged tweets could be used to understand traffic events in India. We developed a novel framework that does not only categorize traffic related tweets but also extracts the locations of the traffic events from the tweet content in Greater Mumbai. The results show that an SVM based model performs best detecting traffic related tweets. While extracting location information, a hybrid georeferencing model consists of a supervised learning algorithm and a number of spatial rules outperforms other models. The results suggest people in India, especially in Greater Mumbai often share traffic information along with location mentions, which can be used to complement existing physical transport infrastructure in a cost-effective manner to manage transport services in the urban environment.

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confidence: 99%

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Das

Purves

2020

IEEE Trans. Intell. Transport. Syst.

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“…However, location is not usually shared because of privacy or battery consumption. Some studies (Weidemann, 2014;Cebeillac and Rault, 2016) showed that the number of geolocated tweets is usually between 10% and 20%, but in our experience this number can be much lower, less than 5% in some contexts like politics. So, except some studies that use geolocation data to filter out unwanted messages (Poblete et al, 2011), most of them indicate that the users location is a field that determines with higher precision the geographical position of an user, and therefore their environment.…”

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confidence: 99%

Opinion Mining in Social Networks versus Electoral Polls

Fernández

Llopis

Gutiérrez

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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The recent failures of traditional poll models, like the predictions in United Kingdom with the Brexit, or in United States presidential election with the victory of Donald Trump, have been noteworthy. With the decline of traditional poll models and the growth of the social networks, automatic tools are gaining popularity to make predictions in this context. In this paper we present our approximation and compare it with a real case: the 2017 French presidential election.

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“…Por un lado, la existencia de barreras tecnológicas para aplicar y procesar este tipo de información, ya sea por no contar con los equipos necesarios o por falta de conocimientos específicos para ello; por otro lado, las barreras relacionadas con la disponibilidad de los datos, que suelen estar en control de empresas privadas -que los procesan con objetivos comerciales antes que con fines académicos o de planificación urbana-, con potestad para no compartir información o incluso bloquear el acceso a ella (Gutiérrez Puebla, 2018). Otras limitaciones se vinculan con la falta de representatividad o exhaustividad de los datos, que pueden generar sesgos en los resultados (Cebeillac y Rault, 2016) o la falta de resolución o precisión espacial para llevar a cabo estudios específicos (Gutiérrez Puebla et al, 2019;McNeill et al, 2016). Por último, tal como señalan Cebeillac y Rault (2016) es de destacar la necesidad de debates éticos acerca del uso de esta información, ya que con frecuencia los usuarios aceptan los términos y condiciones de los sitios web, plataformas o aplicaciones, desconociendo que dicha información será utilizada para otros fines.…”

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Transporte y territorio urbano: condiciones de accesibilidad en el aglomerado Gran Buenos Aires

Belogi

Mera²

2022

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En el marco de los estudios sobre la accesibilidad, este artículo se propone abordar las condiciones que brinda el transporte público —entendido como estructura de oportunidades para el acceso a los bienes y servicios urbanos—, tomando como universo espacial al aglomerado Gran Buenos Aires. A partir de una herramienta de Google que recupera datos de la red de transporte público en sus múltiples modos, se construye un indicador (el tiempo de viaje al centro) que permite trabajar a escala metropolitana y dar cuenta, al mismo tiempo, de los diferenciales microespaciales que se configuran a su interior, en diálogo con los procesos de expansión urbana y los patrones de distribución espacial socioeconómica. Se observa, así, que la forma desigual en la que se distribuyen las redes de transporte público configura una estructura de oportunidades restrictiva para la población que reside en áreas periféricas del aglomerado y en los intersticios de sus ejes de crecimiento, contribuyendo a la reproducción de las desigualdades socio-espaciales.

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Contribution of geotagged Twitter data in the study of a social group’s activity space

Cited by 8 publications

References 19 publications

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Opinion Mining in Social Networks versus Electoral Polls

Transporte y territorio urbano: condiciones de accesibilidad en el aglomerado Gran Buenos Aires

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