Uncovering spatiotemporal and semantic aspects of tourists mobility using social sensing

Ferreira, Ana Paula Gomes; Silva, Thiago H.; Loureiro, Antônio A. F.

doi:10.1016/j.comcom.2020.06.005

Cited by 24 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They show, among other results, that the ability to explore the cultural characteristics of each nationality in different destinations leads a promising way to improve recommendation systems for points of interest and other services to particular groups of tourists. Ferreira et al [15] show that it is possible to go one step forward in the understanding of tourists' mobility, identifying where and when places are more important to users in different cities, by using social media data.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Comparing Global Tourism Flows Measured by Official Census and Social Sensing

Skora¹,

Senefonte²,

Delgado³

et al. 2022

Preprint

View full text Add to dashboard Cite

A better understanding of the behavior of tourists is strategic for improving services in the competitive and important economic segment of global tourism. Critical studies in the literature often explore the issue using traditional data, such as questionnaires or interviews. Traditional approaches provide precious information; however, they impose challenges to obtaining large-scale data, making it hard to study worldwide patterns. Location-based social networks (LBSNs) can potentially mitigate such issues due to the relatively low cost of acquiring large amounts of behavioral data. Nevertheless, before using such data for studying tourists' behavior, it is necessary to verify whether the information adequately reveals the behavior measured with traditional data -considered the ground truth. Thus, the present work investigates in which countries the global tourism network measured with an LBSN agreeably reflects the behavior estimated by the World Tourism Organization using traditional methods. Although we could find exceptions, the results suggest that, for most countries, LBSN data can satisfactorily represent the behavior studied. We have an indication that, in countries with high correlations between results obtained from both datasets, LBSN data can be used in research regarding the mobility of the tourists in the studied context.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The first one is composed of check-ins grouped by users obtained from the location-based social network Foursquare 1 . The same dataset was also used in [14,15]. It represents globally shared data spanning four months, April-July of 2014.…”

Section: Data Descriptionmentioning

confidence: 99%

Comparing Global Tourism Flows Measured by Official Census and Social Sensing

Skora¹,

Senefonte²,

Delgado³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Due to the common characteristics between these data and traditional remote sensing data, the social sensing method has been introduced as a supplement to remote sensing to reveal socio-economic characteristics of urban areas. This method has great potential for conducting urban social research combined with new methods [14,15].…”

Section: Introductionmentioning

confidence: 99%

Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data

et al. 2021

View full text Add to dashboard Cite

The urban functional area is critical to an understanding of the complex urban system, resource allocation, and management. However, due to urban surveys’ focus on geographic objects and the mixture of urban space, it is difficult to obtain such information. The function of a place is determined by the activities that take place there. This study employed mobile phone signaling data to extract temporal features of human activities through discrete Fourier transform (DFT). Combined with the features extracted from the point of interest (POI) data and Sentinel images, the urban functional areas of Changchun City were identified using a random forest (RF) model. The results indicate that integrating features derived from remote sensing and social sensing data can effectively improve the identification accuracy and that features derived from dynamic mobile phone signaling have a higher identification accuracy than those derived from POI data. The human activity characteristics on weekends are more distinguishable for different functional areas than those on weekdays. The identified urban functional layout of Changchun is consistent with the actual situation. The residential functional area has the highest proportion, accounting for 33.51%, and is mainly distributed in the central area, while the industrial functional area and green-space are distributed around.

show abstract

“…Na área de turismo esse tipo de aplicação é bem importante. As recomendações a serem feitas podem ser oriundas de diferentes análises como a hora do dia, a disponibilidade do turista em explorar determinada área, seus interesses, seu perfil e suas crenças [3,6,10]. Assim, o problema que este trabalho trata é a análise de popularidade de um evento turístico utilizando dados de redes sociais on-line.…”

Section: Introductionunclassified

“…A diversificação de análises de usuários em sistemas voltados para o setor de turismo é um assunto relevante, pois é uma área importante da economia e de profundo impacto no planejamento urbano [10]. Esses modelos, que são complexos, possuem variáveis intrínsecas e extrínsecas ao turista, como suas crenças [6,10], e com o trânsito local, respectivamente.…”

Section: Introductionunclassified

Analyzing A Touristic Event Popularity Using Social Networks

Lopes

Marques-Neto

2021

Anais Estendidos Do XXVII Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia 2021)

View full text Add to dashboard Cite

Context-aware recommendation systems use contextual data to recommend a fully personalized suggestion to their users, for instance, using the massive workloads produced by the usage of mobile apps. In this paper, we collected and analyzed a dataset from social media related to the Belo Horizonte’s 2020 Carnival to understand how this event attracts tourists (or Belo Horizonte’s non-resident), analyzing their interactions with a large recommendation system. We built a point-of-view of an event mixing its features in two social networks: Twitter and Google Review. Our results show that combining traits lead to better information about the given context using social networks. It helps both the tourists choosing where to travel and the local establishments to provide better services.

show abstract

Uncovering spatiotemporal and semantic aspects of tourists mobility using social sensing

Cited by 24 publications

References 26 publications

Comparing Global Tourism Flows Measured by Official Census and Social Sensing

Comparing Global Tourism Flows Measured by Official Census and Social Sensing

Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data

Analyzing A Touristic Event Popularity Using Social Networks

Contact Info

Product

Resources

About