Chapter 5 Tracking via Geotagged Social Media Data
Published: Nov 2020
Component type: chapter
Published in: Tracking Tourists
Parent DOI: 10.23912/9781911635383-4277
Over the past twenty years, social media has changed the ways in which we plan, travel and reflect on our travels. Tourists use social media while travelling to stay in touch with friends and family, enhance their social status (Guo et al., 2015); and assist others with decision making (Xiang and Gretzel, 2010; Yoo and Gretzel, 2010). They also use it to report back to their friends and family where they are. This can be done using a geotag function that provides a location for where a post is made. While little is known about why tourists choose to geotag their social media posts, Chung and Lee (2016) suggest that geotags may be used in an altruistic manner by tourists, in order to provide information, and because they elicit a sense of anticipated reward. What is known, however, is that the function offers researchers the ability to understand where tourists travel. There are two types of geotagged social media data. The first of these is discussed in this chapter and may be defined as single point geo-referenced data – geotagged social media posts whose release is chosen by the user. This includes data gathered from social media apps such as Facebook, Instagram, Twitter and WeiChat. The method of obtaining this data involves the collation of large numbers of discrete geotagged updates or photographs. Data can be collated via an application programming interface (API) provided by the app developer to researchers, by automated data scraping via computer programs, perhaps written in Python, or manually by researchers. The second type of data is continuous location-based data from applications that are designed to track movement constantly, such as Strava or MyFitnessPal. Tracking methods using this continuous location-based data are discussed in detail in the following chapter.
- Anne Hardy, University of Tasmania (Author) https://orcid.org/0000-0003-1461-2967
For the source title:
- Anne Hardy, University of Tasmania (Author) https://orcid.org/0000-0003-1461-2967
Hardy, A. (2020) "Chapter 5 Tracking via Geotagged Social Media Data" In: Hardy, A. (ed) . Oxford: Goodfellow Publishers http://dx.doi.org/10.23912/9781911635383-4575
Chua, A., Servillo, L., Marcheggiani, E. and Vande Moere, A. (2016) Mapping Cilento: Using geotagged social media data to characterize tourist flows in Southern Italy. Tourism Management, 57, 295-310
Chung, N and Lee, H. (2016) Sharing economy in geotag: what are the travelers' goals in sharing their locations by geotags in social network sites during the tour?, International Journal of Tourism Cities, 2(2), 125-136.
Commonwealth of Australia (2018) National Statement on Ethical Conduct in Human Research, . The National Health and Medical Research Council, the Australian Research Council and Universities Australia.
Cristina, L. and Stoleriu, O.M (2020) Spatial patterns of tourists preferences in Romanian cities using TripAdvisor, In Stienmetz, Ferrer-Rosell & Schuckert (eds.) Proceedings of the ENTER2020 Ph.D. Workshop, Surrey, England.
Diamantini, C. Genga, L. Marozzo, F. Potena, D. and Trunfio, P. (2017) Discovering mobility patterns of instagram users through process mining techniques, In Proceedings of the 2017 IEEE International Conference on Information Reuse and Integration (IRI), San Diego, 485-492.
Dickinger, A., Scharl, A., Stern, H., Weichselbraun, A. and Wöber, K. (2008) Acquisition and relevance of geotagged information in tourism, In P. O'Connor., W. Hopken & U. Gretzel (Eds.), Proceedings of Information and Communication Technologies in Tourism 2008, Springer, 545-555.
Garcia-Palomores, J., Gutierrez,J. and Minguez, C. (2015) Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS, Applied Geography, 63, 408-417.
Girardin, F., Calabrese, F., Fiore, F.D., Ratti, C. and Blat, J. (2008) Digital footprinting: uncovering tourists with user-generated content, Pervasive Computing, 7(4), 36-43. Gavric, K.D., Culibrk, D.R., Lugonja, P.I., Mirkovic, M.R. and Crnojevic, V.S. (2011) Detecting attractive locations and tourists' dynamics using geo- referenced images. 10th International Conference on Telecommunication in Modern Satellite Cable and Broadcasting Services (TELSIKS), 208-211, Belgrade, Oct 5-8.
Gikas, J. and Grant, M. (2013) Mobile computing devices in higher education: student perspectives on learning with cellphones, smartphones and social media, The Internet and Higher Education, 19, 18-26.
Gou, S., Li, G., Zhang, K., Liang, Y. and Zhou, J. (2016) Space-time behaviour of "tourists" based on self-media platform: A case study of Professor W's WeChat moments, Tourism Tribune, 31(8), 71-80.
Gretzel, U. and Hardy, A., (2019) # VanLife: Materiality, makeovers and mobility amongst digital nomads. E-review of Tourism Research, 16(2/3), 1-9.
Guo, L., Li, Z., and Sun, W. (2015). Understanding travel destination from structured tourism blogs. In Proceedings of 2015 Wuhan International Conference on e-Business (pp. 144-151).
Hardy, A. and Dolnicar, S. (2018) Networks and hosts: a love-hate relationship, In S Dolnicar (ed) Peer-to-Peer Accommodation Networks: Pushing the boundaries, Goodfellow Publishers, Oxford, pp. 182-194. Hausmann, A., T. Toivonen, R. Slotow, H. Tenkanen, A. Moilanen, V.
Heikinheimo and E. Di Minin. (2017) Social media data can be used to understand tourists' preferences for nature-based experiences in protected areas, Conservation Letters, 11(1), 1-10. Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P. and Ratti, C. (2014) Geo-located Twitter as proxy for global mobility patterns, Cartography and Geographic Information Science, 41, 260-271.
Jiang, K., Wang, P. and Yu, N. (2011) ContextRank: Personalized tourism recommendation by exploiting context information of geotagged web photos, in Proceedings of IEEE16th International Conference on Image and Graphics, 931-937.
Kachkaev, A. and Wood, J. (2013). Investigating spatial patterns in user- generated photographic datasets by means of interactive visual analytics. Paper presented at the GeoViz Hamburg: Interactive Maps that Help People Think, 6-8 Mar, HafenCity University, Hamburg, Germany
Kádár, B. (2014) Measuring tourist activities in cities using geotagged photography, Tourism Geographies, 16 (1), 88-104.
Kádár, B. and Gede, M. (2013) Where do tourists go? Visualizing and analysing the spatial distribution of geotagged photography, Cartographica, 48(2),78-88.
Kisilevich, S., Keim, D., Andrienko, N., Andrienko, G. (2013) Towards acquisition of semantics of places and events by multi-perspective analysis of geotagged photo collections, in A. Moore and I. Drecki (Eds.), Geospatial Visualisation, Lecture Notes in Geoinformation and Cartography, Springer- Verlag, Berlin Heidelberg
Koerbitz, W., Önder, I. and Hubmann-Haidvogel, A.C. (2013) Identifying tourist dispersion in Austria by digital footprints, in L. Cantoni, Z. Xiang (Eds.), Information and Communication Technologies in Tourism 2013, Springer Verlag Berlin Heidelberg, 495-506.
Kozinets, R. (2019) Netnography: The Essential Guide to Qualitative Social Media Research. Third Edition, SAGE Publications.
Levin, N., Lechner, A.M. and Brown, G. (2017) An evaluation of crowdsourced information for assessing the visitation and perceived importance of protected areas, Applied Geography, 79, 115-126.
Levin, N., Kark, S. and Crandall, D. (2015) Where have all the people gone? Enhancing global conservation using night lights and social media, Ecological Applications, 25, 2153-2167.
Mukhina, K.D., Rakitin, S. and Vishertin, A. (2017) Detection of tourists attraction points using Instagram profiles, in Proceedings of the International Conference on Computational Science, Zurich Switzerland, 2378-2382.
Popescu, A., Grefenstette, G. and Moëllic, P.A. (2009) Mining tourist information from user-supplied collections, Paper presented at the Conference on Information and Knowledge Management, http://comupedia. org/adrian/articles/sp0668-popescu.pdf [Accessed 6th May 2020]
Ramasco, J. J. (2016) Touristic site attractiveness seen through Twitter, EPJ Data Science, 5(1), 12.
Richards, D.R. and D.A. Friess (2015) A rapid indicator of cultural ecosystem service usage at a fine spatial scale: Content analysis of social media photographs, Ecological Indicators, 53, 187-195.
Rossi, L.; Boscaro, E.; Torsello, A. (2018) Venice through the lens of Instagram: A visual narrative of tourism in Venice. In Proceedings of the Companion of the Web Conference, Lyon, France, 1190-1197. Salas-Olmedo, M.H., Moya-Gómez, B., García-Palomares, J.C. and Gutiérrez,
J. (2018) Tourists' digital footprint in cities: Comparing Big Data sources, Tourism Management, 66, 13-25. See, L., P. Mooney, G. Foody, L. Bastin, A. Comber, J. Estima, S. Fritz, N. Kerle et al. (2016) Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information, International Journal of Geo-Information, 5 (5), 55.
Sobolevsky, S., Bojic, I., Belyi, A., Sitko, I., Hawelka, B. and Arias, J. M.(2015) Scaling of city attractiveness for foreign visitors through big data of human economical and social media activity, in Big data (BigData Congress), 2015 IEEE International Congress, 600-607.
Sonter, L.J., Watson, K.B., Wood, S.A. and Ricketts, T.H. (2016) Spatial and temporal dynamics and value of nature-based recreation, estimated via social media, PLoS One, 11(9), doi:10.1371/ journal.pone.0162372 0162372. Spalding, M., Burke, L., Wood, S.A., Ashpole, J., Hutchison, J. and Ermgassen, P. (2017) Mapping the global value and distribution of coral reef tourism, Marine Policy, 82, 104-113.
Straumann, R.K., Çöltekin, A. and Andrienko, G. (2014) Towards (re) constructing narratives from georeferenced photographs through visual analytics', The Cartographic Journal, 51(2), 152-165.
Tammet, T., Luberg, A. and Järv, P. (2013). Sightsmap: crowd-sourced popularity of the world places. In Information and Communication Technologies in Tourism 2013. Springer Verlag, Berlin, Heidelberg, pp. 314-325. Tenkanen, T., Di Minin, E. Heikinheimo, V., Hausmann,A., Her, M., Kajala, L. and Toivonen. T. (2017) Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas, Scientific Reports, 7(1), 1-11.
The Telegraph (2019) Snapchat adds end-to-end encryption to protect users' messages, https://www.telegraph.co.uk/technology/2019/01/09/snapchat- adds-end-to-end-encryption-protect-users-messages/ [Accessed 27th August 2019]
Townsend, L. and Wallace, C. (2016) Social media research: A guide to ethics, Working paper published by the University of Aberdeen https://www.gla. ac.uk/media/Media_487729_smxx.pdf. [Accessed 4th February 2020]
Trip Advisor (2020) Media Centre. Available from https://tripadvisor.mediaroom.com/ie-terms-of-use [Accessed 5th August 2020]
Twitter, (2020)Twitter terms of service. Available from https://twitter.com/en/tos [Accessed 5th August 2020]
van der Zee, E., Bertocchi, D. and Vanneste, D. (2020) Distribution of tourists within urban heritage destinations: a hot spot/cold spot analysis of TripAdvisor data as support for destination management, Current Issues in Tourism, 23(2), 175-196.
Vu, H., Li, G, Law, R. and Ye, B. (2015) Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos, Tourism Management, 46, 222-232. Walden-Schreiner, C., Dario Rossi, S., Barros, A. Pickering, C. and Leung, Y. (2018) Using crowd-sourced photos to assess seasonal patterns of visitor use in mountain-protected areas, Ambio, 47, 781-793.
WeChat. (2020) Acceptable Use Policy. [Available from https://www.wechat. com/en/acceptable_use_policy.html [Accessed 26th March 2020]
Wong, E., Law R., Li G. (2017) Reviewing geotagging research in tourism. In: Schegg R., Stangl B. (eds) Information and Communication Technologies in Tourism 2017. Springer, Cham.
Xiang, Z. and Gretzel, U., (2010) Role of social media in online travel information search. Tourism Management, 31 (2), 179-188.
Yoo, K. H. and Gretzel, U. (2010). Antecedents and impacts of trust in travel- related consumer-generated media. Information Technology & Tourism, 12(2), 139-152.
Zheng, Y. T., Zha, Z. J. and Chua, T. S. (2012) Mining travel patterns from geotagged photos, Transactions on Intelligent Systems and Technology, 3(3), 56-73.
Zhou, B., Liu, L., Oliva, A. and Torralba, A. (2014). Recognizing city identity via attribute analysis of geotagged images. In Fleet , D., Pajdla, T., Schiele, B., Tuytelaars, T. (Eds), European Conference on Computer Vision-ECCV 2014 Proceedings, Springer International Publishing, 519-534.