Predicting mobility patterns based on profiles of social media users: tourists case study

  • Helen C. de Mattos Senefonte UEL
  • Thiago H. Silva UTFPR
  • Ricardo Lüders UTFPR


Studies based on traditional data sources like surveys, for instance, offer poor scalability. The experiments are limited, and the results are restricted to small regions (such as a city or a state). The use of location-based social network (LBSN) data can mitigate the scalability problem by enabling the study of social behavior in large populations. When explored with Data Mining and Machine Learning techniques, LBSN data can be used to provide predictions of relevant cultural and behavioral data from cities or countries around the world. The main goal of this work is to predict and explore user behavior from LBSNs in the context of tourists’ mobility patterns. To achieve this goal, we propose PredicTour, which is an approach used to process LBSN users’ check-ins and to predict mobility patterns of tourists with or without previous visiting records when visiting new countries. PredicTour is composed of three key blocks: mobility modeling, profile extraction, and tourist’ mobility prediction. In the first block, sequences of check-ins in a time interval are associated with other user information to produce a new structure called "mobility descriptor”. In the profile extraction, self-organizing maps and fuzzy C-means work together to group users according to their mobility descriptors. PredicTour then identifies tourist’ profiles and estimates their mobility patterns in new countries. When comparing the performance of PredicTour with three well-known machine learning-based models, the results indicate that PredicTour outperforms the baseline approaches. Therefore, it is a good alternative for predicting and understanding international tourists’ mobility, which has an economic impact on the tourism industry, particularly when services and logistics across international borders should be provided. The proposed approach can be used in different applications such as recommender systems for tourists, and decision-making support for urban planners interested in improving both the tourists’ experience and attractiveness of venues through personalized services.
Palavras-chave: location-based social network, machine learning, tourist mobility, cross-cultural analysis, social computing


Ayoub Arroub, Bassma Zahi, Essaid Sabir, and Mohamed Sadik. 2016. A literature review on Smart Cities: Paradigms, opportunities and open problems. WINCOM. Fez, Morocco. (2016), 180–186.

H. C. de Mattos Senefonte, T. H. Silva, R. Lüders, and M. R. B. S. Delgado. 2019. Classifying Venue Categories of Unlabeled Check-ins Using Mobility Patterns. In 15th DCOSS. 562–569.

Ana P.G. Ferreira, Thiago H. Silva, and Antonio A.F. Loureiro. 2020. Uncovering spatiotemporal and semantic aspects of tourists mobility using social sensing. Computer Communications 160 (2020), 240 – 252.

A. Lew and B. McKercher. 2006. Modeling tourist movements: A local destination analysis. Annals of Tourism Research 33, 2 (2006), 403–423.

World Tourism Organization. 2021. International Tourism Highlights, 2020 Edition. World Tourism Organization, Madrid, Spain.

Helen Senefonte, Gabriel Frizzo, Myriam Delgado, Ricardo Lüders, Daniel Silver, and Thiago Silva. 2020. Regional Influences on Tourists Mobility Through the Lens of Social Sensing. In Social Informatics. Cham, 312–319.

Helen C. de Mattos SENEFONTE. 2022. Predicting mobility patterns based on profiles of social media users: tourists case study. Ph.D. Dissertation. Graduate Program in Electrical and Computer Engineering of the Universidade Tecnológica Federal do Paraná - Curitiba.

Helen C. Mattos Senefonte, Myriam Regattieri Delgado, Ricardo Lüders, and Thiago H. Silva. 2022. PredicTour: Predicting Mobility Patterns of Tourists Based on Social Media User’s Profiles. IEEE Access 10 (2022), 9257–9270.

Thiago H. Silva, Pedro O. S. Vaz de Melo, Jussara M. Almeida, and Antonio Alfredo Ferreira Loureiro. 2014. Large-scale study of city dynamics and urban social behavior using participatory sensing. IEEE Wireless Communications 21 (2014), 42–51.

Thiago H. Silva, Pedro O. S. Vaz de Melo, Jussara M. Almeida, Juliana F. S. Salles, and Antonio Alfredo Ferreira Loureiro. 2014. Revealing the City That We Cannot See. ACM Trans. Internet Techn. 14 (2014), 26:1–26:23.

Lucas Skora, Helen Senefonte, Myriam Delgado, Ricardo Lüders, and Thiago Silva. 2021. Mobilidade de Turistas Internacionais: Uma Comparação entre Dados Oficiais e de LBSN. In Anais do V CoUrb (Uberlândia). SBC, 112–125.

Lucas E.B. Skora, Helen C.M. Senefonte, Myriam Regattieri Delgado, Ricardo Lüders, and Thiago H. Silva. 2022. Comparing global tourism flows measured by official census and social sensing. Online Social Networks and Media 29 (2022), 100204.

David Veiga, Gabriel Frizzo, Helen Senefonte, Ricardo Lüders, Myriam Delgado, and Thiago Silva. 2020. Influências Regionais na Mobilidade de Turistas e Residentes Usando Dados de Mídia Social. In Anais do XXXVIII SBRC (Rio de Janeiro). SBC, Porto Alegre, Brasil, 589–602.

Huy Quan Vu, Gang Li, and Rob Law. 2020. Cross-Country Analysis of Tourist Activities Based on Venue-Referenced Social Media Data. Journal of Travel Research 59, 1 (2020), 90–106.
SENEFONTE, Helen C. de Mattos; SILVA, Thiago H.; LÜDERS, Ricardo. Predicting mobility patterns based on profiles of social media users: tourists case study. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 41-44. ISSN 2596-1683. DOI: