Social media data as a user perception tool: Evaluation of user experiences in Sultanahmet Area

Year 2021, Volume: 24 Issue: 45, 243 - 260, 30.06.2021

Ezgi Güler Tozluoğlu , Caglar Tozluoglu , Dilcan Güler , Mehmet Emre Güler

https://doi.org/10.31795/baunsobed.854753

Cited By: 1

Abstract

Social media and new communication technologies have been developing rapidly in recent years and contribute to urban studies. The massive data provided by mobile devices and web services remark a new information source that can be functional in city-specific decision-making. With these features, social networks can show urban life's situation about each user's unique social, economic, and political aspects. In this context, data obtained from new media and social networks in planning the cities' touristic areas will contribute to regional and local tourism planning. This study focuses on the analysis process, evaluation, and contribution of the data set obtained from the Flickr and Foursquare application, one of the location-based social networks, to urban design and tourism studies. Each social media application was evaluated within itself and a holistic evaluation was made in the city square with the data obtained between 2004-2018. In the research area designated as Sultanahmet Square, the experiences and perceptions of individuals using the specified web applications were examined.

Keywords

Turizm, Urban Square, Social Media, Data Analysis, Sultanahmet Square

Kullanıcı algısı aracı olarak sosyal medya verileri: Sultanahmet Bölgesi’ndeki kullanıcı deneyimlerinin değerlendirilmesi

Abstract

Sosyal medya ve yeni iletişim teknolojileri son yıllarda hızla gelişmekte ve kent çalışmalarına katkı sağlamaktadır. Mobil cihazlar ve web hizmetleri tarafından sağlanan büyük veriler, kente yönelik karar verme mekanizmalarında işlevsel olabilecek yeni bir bilgi kaynağına işaret etmektedir. Bu özellikleri ile sosyal ağlar, kent ve kentteki her kullanıcının özgün sosyal, ekonomik ve politik yönleri hakkındaki durumunu göstermektedir. Bu kapsamda şehirlerin turistik alanlarının planlanmasında yeni medya ve sosyal ağlardan elde edilen veriler bölgesel ve yerel turizm planlamasına katkı sağlayacak özelliktedir. Bu çalışma, konum temelli sosyal ağlardan biri olan Flickr ve Foursquare uygulamasından elde edilen veri setinin analiz süreci, değerlendirilmesi ve kent ve turizm çalışmalarına katkısına odaklanmaktadır. 2004-2018 yılları arasında elde edilen veriler ile her sosyal medya uygulaması kendi içinde değerlendirilerek kent meydanında bütüncül bir değerlendirme yapılmaktadır. Sultanahmet Meydanı özelinde, belirtilen web uygulamalarını kullanan bireylerin deneyimleri ve paylaşımları üzerinden kullanıcı algıları incelenmektedir.

Keywords

Turizm, Kent Meydanı, Sosyal Medya, Veri Analizi, Sultanahmet Meydanı

References

• Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: capitalizing on big data. Journal of Travel Research, 58(2), 175-191.
• Asakura, Y., & Iryo, T. (2007). Analysis of tourist behaviour based on the tracking data collected using a mobile communication instrument. Transportation Research Part A: Policy and Practice, 41(7), 684-690.
• Batty, M. (1990). Invisible cities. Environment and Planning B: Planning and Design, 17(2):127-130.
• Batty, M. (1997). The computable city. International PlanningStudies, 2(2):155–173.
• Bawa-Cavia, A., 2011. Sensing the urban: using location-based social network data in urban analysis. In: The first workshop on pervasive urban applications (PURBA), 12–15 June, San Francisco, CA.
• Borrego-Jaraba, F., Ruiz, I. L., & Gómez-Nieto, M. Á. (2011). A NFC-based pervasive solution for city touristic surfing. Personal and ubiquitous Computing, 15(7), 731-742.
• Cerrone, D., (2015). A sense of place. Turku Urban Research Programmes. Research Report 1/2015. Available from: http://beta.turku.fi/sites/default/files/atoms/files/
• Chareyron, G., Da-Rugna, J., & Raimbault, T. (2014, October). Big data: A new challenge for tourism. In 2014 IEEE International Conference on Big Data (Big Data) (pp. 5-7). IEEE.
• Chua, A., Servillo, L., Marcheggiani, E., & Moere, A. V. (2016). Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy. Tourism Management, 57, 295-310.
• Ciuccarelli, P., Lupi, G., and Simeone, L., 2014. Visualizing the data city. In: Social media as a source of knowledge for urban planning and management. Heidelberg: Springer
• Cranshaw, J., Schwartz, R., Hong, J., & Sadeh, N. (2012, May). The livehoods project: Utilizing social media to understand the dynamics of a city. In Sixth International AAAI Conference on Weblogs and Social Media, 58-65.
• Del Vecchio, P., Mele, G., Ndou, V., & Secundo, G. (2018). Creating value from social big data: Implications for smart tourism destinations. Information Processing & Management, 54(5), 847-860.
• Egger, R. (2013). The impact of near field communication on tourism. Journal of Hospitality and Tourism Technology. 4 (2), 119-133.
• Elwood, S., Goodchild, M. F., & Sui, D. Z., (2012). Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the Association of American Geographers, 102(3), 571-590.
• Frias-Martinez, V., Soto, V., Hohwald, H., & Frias-Martinez, E. (2012). Characterizing urban landscapes using geolocated tweets. In 2012, September, International conference on privacy, security, risk and trust and 2012 international confernece on social computing, pp. 239-248 IEEE.
• Fuchs, M., Höpken, W., & Lexhagen, M. (2014). Big data analytics for knowledge generation in tourism destinations–A case from Sweden. Journal of Destination Marketing & Management, 3(4), 198-209.
• García-Palomares, J. C., Gutiérrez, J., & Mínguez, 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. et al. , 2009, Quantifying urban attractiveness from the distribution and density of digital footprints. Int. J. Spat. Data Infrastructures Res. 4, 175–200.
• Gretzel, U. (2011). Intelligent systems in tourism: A social science perspective. Annals of tourism research, 38(3), 757-779.
• Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information systems, 47, 98-115.
• Heikinheimo, V. et al., 2017, User-Generated Geographic Information for Visitor Monitoring in a National Park: A Comparison of Social Media Data and Visitor Survey. ISPRS Int. J. Geo-Information 6, 85.
• Hollenstein, L., & Purves, R. (2010). Exploring place through user-generated content: Using Flickr tags to describe city cores. Journal of Spatial Information Science, 2010(1), 21-48.
• Irudeen, R., & Samaraweera, S. (2013, December). Big data solution for Sri Lankan development: A case study from travel and tourism. In 2013 International Conference on Advances in ICT for Emerging Regions (ICTer) (pp. 207-216). IEEE.
• Lazer, D., ve diğ., 2009. Computational social science. Science, 323 (5915), 721–723. DOI:10.1126/ science.1167742
• Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, 68, 301-323.
• Noulas, A., 2011. An empirical study of geographic user activity patterns in Foursquare. In: Proceedings of the fifth international AAAI conference on weblogs and social media, 17–21 July, Barcelona. Menlo Park, CA: The AAAI Press, 570–573.
• Nummi, P. (2017). Social media data analysis in urban e-planning. International Journal of E- Planning Research (IJEPR), 6(4), 18-31.
• Offenhuber, D., and Ratti, C., 2014. Decoding the city. In: Urbanism in the age of big data. Basel: Birkhäuser Verlag Gmbh
• Shao, H., Zhang, Y., & Li, W. (2017). Extraction and analysis of city’s tourism districts based on social media data. Computers, Environment and Urban Systems, 65, 66-78.
• Silva, T.H., 2013. A comparison of Foursquare and Flickr to the study of city dynamics and urban social behavior. In: Proceedings of the 2nd ACM SIGKDD international workshop on urban computing, 11–14 August, Chicago, IL. New York: ACM.
• WeAreSocial. (2017). Digital in 2017. Retrieved May 20, 2018, from https://wearesocial.com/uk/special-reports/digital-in-2017-global-overview.
• Taras Agryzkov, Pablo Martí, Leandro Tortosa & José F. Vicent (2016): Measuring urban activities using Foursquare data and network analysis: a case study of Murcia (Spain), International Journal of Geographical Information Science, DOI: 10.1080/13658816.2016.1188931
• Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., & Li, L. J. (2015). The new data and new challenges in multimedia research. arXiv preprint arXiv:1503.01817, 1(8).
• Yin, Z., Cao, L., Han, J., Luo, J., & Huang, T. (2011, April). Diversified trajectory pattern ranking in geotagged social media. In Proceedings of the 2011 SIAM International Conference on Data Mining (pp. 980-991). Society for Industrial and Applied Mathematics.
• Zhai, S., Xu, X., Yang, L., Zhou, M., Zhang, L., & Qiu, B. (2015). Mapping the popularity of urban restaurants using social media data. Applied Geography, 63, 113-120.
• Zhan, X. Ukkusuri, S. V. Zhu, F. (2014). Inferring Urban Land Use Using Large-Scale Social Media Check-in Data, Networks, and Spatial Economics, 14(3-4), 647-667.
• Url-1: https://www.statista.com/statistics/570098/distribution-of-social-media-used-turkey/