Location-Based Social Network Data for Tourism Destinations



Social media networks are a resource for valuable knowledge about tourist destinations through the collection of data by Location-Based Social Networks (LBSN). A major problem is the lack of knowledge in respect to the visitors’ views about a destination, as well as the fact that the visitors’ behavior needs and preferences are not visible. Many enterprises and local authorities are still using traditional methods for acquiring knowledge to make strategic decisions, by collecting data from questionnaires. Nonetheless, this process, despite its benefits, is short-lived and the number of the participants is small compared to the number of visitors. This chapter discusses a methodology for the extraction, association, analysis, and visualization of data derived from LBSNs. This provides knowledge of visitor behaviors, impressions and preferences for tourist destinations. A case study of Crete in Greece is included, based upon visitors’ posts and reviews, nationality, photos, place rankings, and engagement.


Big data Tourism Tourist destination Social media Location-based social networks Data visualization 


  1. Bouadi T et al (2017) A data warehouse to explore multidimensional simulated data from a spatially distributed agro-hydrological model to improve catchment nitrogen management. Environ Model Softw 97:229–242CrossRefGoogle Scholar
  2. Brandt T, Bendler J, Neumann D (2017) Social media analytics and value creation in urban smart tourism ecosystems. Inf Manag 54(6):703–713CrossRefGoogle Scholar
  3. Carvalho JP et al (2017) MISNIS: An intelligent platform for twitter topic mining. Expert Syst Appl 89:374–388CrossRefGoogle Scholar
  4. Chang Y-C, Ku C-H, Chen CH (2017) Social media analytics: extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. Int J Info Manag. Available at
  5. Chen H, Chiang RHL, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q Manag Inf Syst 36(4):1165–1188CrossRefGoogle Scholar
  6. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19(2):171–209CrossRefGoogle Scholar
  7. Chorley MJ, Whitaker RM, Allen SM (2015) Personality and location-based social networks. Comput Hum Behav 46:45–56CrossRefGoogle Scholar
  8. Donaire JA, Camprubí R, Galí N (2014) Tourist clusters from Flickr travel photography. Tourism Manag Perspect 11:26–33CrossRefGoogle Scholar
  9. Ferguson R (2008) Word of mouth and viral marketing: taking the temperature of the hottest trends in marketing. J Consum Mark 25(3):179–182CrossRefGoogle Scholar
  10. Ferreira N et al (2013) Visual exploration of big spatio-temporal urban data: a study of New York City Taxi Trips. IEEE Trans Visual Comput Graph 19(12):2149–2158CrossRefGoogle Scholar
  11. Floris R, Campagna M (2014) Social media data in tourism planning: analysing tourists’ satisfaction in space and time. In Schrenk et al M (eds) REAL CORP 2014—PLAN IT SMART! Clever solutions for smart cities. 19th international conference on urban planning and regional development in the information society. CORP—Competence Center of Urban and Regional Planning, pp 997–1003Google Scholar
  12. Günther WA et al (2017) Debating big data: a literature review on realizing value from big data. J Strateg Inf Syst 26(3):191–209CrossRefGoogle Scholar
  13. Hashem IAT et al (2015) The rise of “big data” on cloud computing: review and open research issues. Inf Syst 47:98–115CrossRefGoogle Scholar
  14. Huang A, Gallegos L, Lerman K (2017) Travel analytics: understanding how destination choice and business clusters are connected based on social media data. Transp Res Part C Emerg Technol 77:245–256CrossRefGoogle Scholar
  15. Komorowski M, Do Huu T, Deligiannis N (2018) Twitter data analysis for studying communities of practice in the media industry. Telematics Inform 35(1):195–212CrossRefGoogle Scholar
  16. Lee R, Wakamiya S, Sumiya K (2011) Discovery of unusual regional social activities using geo-tagged microblogs. World Wide Web J Biol 14(4):321–349CrossRefGoogle Scholar
  17. Li D, Zhou X, Wang M (2018) Analyzing and visualizing the spatial interactions between tourists and locals: a Flickr study in ten US cities. Cities. Available at
  18. Liu Y et al (2016) ELAN: an efficient location-aware analytics system. Big Data Res 5:16–21CrossRefGoogle Scholar
  19. Liu H et al (2018) Detecting global and local topics via mining twitter data. Neurocomputing 273:120–132CrossRefGoogle Scholar
  20. Mahmud S, Iqbal R, Doctor F (2016) Cloud enabled data analytics and visualization framework for health-shocks prediction. Future Gener Comput Syst FGCS 65:169–181CrossRefGoogle Scholar
  21. Marchiori E, Cantoni L (2015) The role of prior experience in the perception of a tourism destination in user-generated content. J Destination Market Manag 4(3):194–201CrossRefGoogle Scholar
  22. Marine-Roig E, Clavé SA (2015) Tourism analytics with massive user-generated content: a case study of Barcelona. J Destination Market Manag 4(3):162–172CrossRefGoogle Scholar
  23. Miah SJ et al (2017) A Big Data analytics method for tourist behaviour analysis. Inf Manag 54(6):771–785CrossRefGoogle Scholar
  24. Milton S (2011) Location intelligence—the future looks bright. Forbes. Available at Accessed 18 Jan 2018
  25. Mittal V et al (2017) Multivariate features based instagram post analysis to enrich user experience. Procedia Comput Sci 122:138–145CrossRefGoogle Scholar
  26. Park SB, Jang J, Ok CM (2016) Analyzing Twitter to explore perceptions of Asian restaurants. J Hospitality Tourism Technol 7(4):405–422CrossRefGoogle Scholar
  27. Rathore MM et al (2017) Big data analytics of geosocial media for planning and real-time decisions. In: 2017 IEEE international conference on communications (ICC). Available at
  28. Ravi K et al (2018) Analytics in/for cloud-an interdependence: a review. J Netw Comput Appl 102:17–37CrossRefGoogle Scholar
  29. Rehman NU, Weiler A, Scholl MH (2014) OLAPing Social Media: the case of Twitter. In: 2013 IEEE/ACM international conference on advances in social networks analysis and mining. Knowledge Discovery and Data Mining. ACM, pp 1139–1146Google Scholar
  30. Reinsel D, Gantz J, Rydning J (2017) Data Age 2025: the evolution of data to life-critical. IDCGoogle Scholar
  31. Schmidbauer H, Rösch A, Stieler F (2018) The 2016 US presidential election and media on Instagram: Who was in the lead? Comput Hum Behav 81:148–160CrossRefGoogle Scholar
  32. Shekhar S, Xiong H (2007) Encyclopedia of GIS. Springer Science & Business MediaGoogle Scholar
  33. Uzunoğlu E, Kip SM (2014) Brand communication through digital influencers: leveraging blogger engagement. Int J Inf Manage 34(5):592–602CrossRefGoogle Scholar
  34. Vashisht P, Gupta V (2015) Big data analytics techniques: a survey. In: 2015 international conference on green computing and internet of things (ICGCIoT). Available at
  35. Vassakis K, Petrakis E, Kopanakis I (2017) Big data analytics: applications, prospects and challenges. In: Lecture notes on data engineering and communications technologies, pp 3–20Google Scholar
  36. Vecchio PD et al (2017) Creating value from social big data: implications for smart tourism destinations. Inf Process Manag. Available at
  37. Wang L et al (2015) On the brink: predicting business failure with mobile location-based checkins. Decis Support Syst 76:3–13CrossRefGoogle Scholar
  38. Yoo K-H, Sigala M, Gretzel U (2016) Exploring TripAdvisor. In: Tourism on the verge, pp 239–255CrossRefGoogle Scholar
  39. Zheng Y (2011) Location-based social networks: users. In: Computing with spatial trajectories, pp 243–276CrossRefGoogle Scholar
  40. Zhou X, Xu C, Kimmons B (2015) Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform. Comput Environ Urban Syst 54:144–153CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of CreteRethymnoGreece
  2. 2.TEI of CreteHeraklionGreece

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