Imagine ability, Image of city, Tourist destination, Social media analytics, Data mining, Rasht

Document Type : Original Article

Authors

1 Master of Urban Planning, Faculty of Art and Architecture ,University of Guilan , Rasht, Iran

2 Associate Professor, Department of Urban Planning, Faculty of Art and Architecture, University of Guilan, Rasht, Iran

3 Associate Professor, Department of Geography and urban planning, Faculty of Literature and Human Sciences, University of Guilan, Rasht, Iran

Abstract

Introduction:The tourism destination image is a mental concept formed through the process of processing a large set of diverse informational sources, including beliefs, ideas, perceptions, and individual or group thoughts about a specific place, which develop over time. This is a crucial prelude to tourists' behavioral intentions toward a destination. In this regard, one of the important aspects of destination management is planning and developing its image, and conveying a desirable and positive image of the destination is of great significance. In recent years, with the development of internet technology, destination images have been created based on user-generated content about tourist destinations shared on social media platforms. This type of content operates beyond the efforts of destination marketing organizations. Therefore, valuable insights can be gained by focusing on various aspects of user behavior.
Methodology: This research has a descriptive-analytical nature and is applied to its objective. In the first step of this study, data collection was conducted. Data collection was performed using two methods. In the first method, a systematic approach was employed, utilizing a web crawler programmed in the Python software, to collect data from Twitter. In the second method, without a systematic approach, textual data was collected manually by a human user from the user comments sections of the websites "Google Maps" and "TripAdvisor" regarding the city of Rasht. In the next step, preprocessing of the collected textual data was performed. After preprocessing and normalizing the textual data, they were prepared for the data mining process. Subsequently, data clustering was conducted using the unsupervised "K-means" algorithm. In this technique, using the elbow and silhouette evaluation methods, the optimal value for K was determined to be 3.
Results: Based on the studies conducted in this research, the most important features shaping the tourism destination image-friendly and creative city of Rasht are grouped into three main clusters.Cluster 1 is the most significant and has received the most attention. Based on the most frequently mentioned words, this cluster is categorized into two subgroups: food and cuisine, and parks and gardens. Urban parks and gardens in Rasht, as unique environmental attractions, have been highly regarded by users and are viewed very positively. This potential can be leveraged to develop and enhance the city’s brand. The next most frequently mentioned cluster, Cluster 2, revolves around infrastructure, amenities, accommodation, and hotels. Hotel facilities, staff, and service quality are among the aspects frequently discussed by tourists on social media and the internet as key features of Rasht. However, accommodation and hotel services have received the highest number of negative reviews, indicating weaknesses in this area. To establish a standout image of Rasht as a destination, efforts must be made to improve infrastructure and public services, with a focus on accommodation and hospitality services. Cluster 3 ranks last in prominence. In this cluster, the Rasht Bazaar stands out as a key feature of the city’s image. It is described as a traditional market offering fresh and local products unique to the region. Another highlighted feature is the Municipality Square Complex, which has gained attention in online discussions about Rasht. The architectural style of the Municipality Square, the cultural pedestrian area extending to Sabzeh Meydan, and the proximity of the bazaar to the Municipality Square serve as major identity symbols of Rasht. These areas, characterized by their historical, cultural, and unique architectural features, attract diverse groups of visitors. The vibrant nightlife in the Municipality Square Complex is another notable aspect of its appeal, as perceived by social media users. Additionally, urban parks and gardens in Rasht are prominent in its online image. These open public spaces, enhanced by the city’s moderate climate, natural landscapes, and scenic views, offer unique experiences for tourists. Increasing the number of protected natural habitats within Rasht could serve as a potential strategy to enhance the city’s creative tourism development.
Discussion: The image of a destination results from an evaluative process that is both objective and subjective. It is formed when tourists interact with and experience the destination’s urban environment after arriving. Since altering an established image is challenging, the destination image becomes a crucial and influential factor in shaping tourists' behavioral intentions toward the destination. Essentially, the destination image serves as an initial input for tourists, encompassing a range of diverse elements. Therefore, the image of a destination should reflect its positive, unique, and distinctive features while avoiding exaggeration beyond the destination's actual capacity and potential. Overpromising can lead to negative experiences for tourists upon arrival, ultimately resulting in a poor image. Thus, planning and managing the destination image of the creative city of Rasht should aim to maintain and enhance its positive and appealing features while addressing and resolving existing shortcomings. Based on the findings of this research, the destination image of Rasht in the digital space is shaped by its unique attractions, as well as its cultural, local, and identity-based features. The city's food culture, as an intangible heritage, plays a central role in this context. The destination image of Rasht, as presented online, aligns with its global branding as a UNESCO Creative City of Gastronomy. The emphasis on food, cuisine, and the Rasht Bazaar—primarily associated with the produce and fish markets—underscores this alignment. Overall, the results of this study indicate a relatively positive image of Rasht as a creative city for tourism. In addition to examining the structure of Rasht’s destination image as a case study, this research proposes a methodological framework for analyzing big data from social media related to urban tourism.

Keywords

Main Subjects

References

Abdul-Rahman, M., Chan, E. H., Wong, M. S., Irekponor, V. E., & Abdul-Rahman, M. O. (2021). A framework to simplify pre-processing location-based social media big data for sustainable urban planning and management. Cities, 109, 102986. Doi: 10.1016/j.cities.2020.102986
Abkenar, S. B., Kashani, M. H., Mahdipour, E., & Jameii, S. M. (2021). Big data analytics meets social media: A systematic review of techniques, open issues, and future directions. Telematics and informatics, 57, 101517. Doi: 10.1016/j.tele.2020.101517
Akehurst, G. (2009). User generated content: the use of blogs for tourism organisations and tourism consumers. Service business, 3(1), 51-61. Doi: 10.1007/s11628-008-0054-2
Alarcon-Urbistondo, P., Rojas-de-Gracia, M. M., & Casado-Molina, A. (2023). Proposal for employing user-generated content as a data source for measuring tourism destination image. Journal of Hospitality & Tourism Research,  Vol 47(4), 643-664. Doi: 10.1177/10963480211012756
Alcázar, M. D. C. H., Piñero, M. S., & de Maya, S. R. (2014). The effect of user-generated content on tourist behavior: the mediating role of destination image. Tourism & Management Studies, 10, 158-164. URL: https://www.tmstudies.net/index.php/ectms/article/view/616
Arabadzhyan, A., Figini, P., & Vici, L. (2021). Measuring destination image: a novel approach based on visual data mining. A methodological proposal and an application to European islands. Journal of Destination Marketing & Management, 20, 100611. Doi: 10.1016/j.jdmm.2021.100611
Arefieva, V., Egger, R., & Yu, J. (2021). A machine learning approach to cluster destination image on Instagram. Tourism Management, 85, 104318. Doi: 10.1016/j.tourman.2021.104318
Assael, H. (1992) Consumer Behavior and Marketing. 3rd ed. Boston Kent Publishing Co, 10-67. URL: https://archive.org/details/consumerbehavior0000assa
Baltas, A., Kanavos, A., & Tsakalidis, A. K. (2016). An apache spark implementation for sentiment analysis on twitter data. In International Workshop of Algorithmic Aspects of Cloud Computing Conference, 22–26. Doi: 10.1007/978-3-319-57045-7_2
Basaran, U. (2016). Examining the relationships of cognitive, affective, and conative destination image: A  research on Safranbolu, Turkey. International Business Research, 9(5), 164-179. Doi: 10.5539/ibr.v9n5p164
Becken, S., Alaei, A. R., & Wang, Y. (2020). Benefits and pitfalls of using tweets to assess destination sentiment. Hospitality and Tourism Technology, 11(1), 19-34. Doi: 10.1108/JHTT-09-2017-0090
Beerli, A., & Martin, J. D. (2004). Factors influencing destination image. Annals of tourism research, 31(3), 657-681. Doi: 10.1016/j.annals.2004.01.010
Bella, E., Leporatti, L., & Maggino, F. (2018). Big data and social indicators: Actual trends and new perspectives. Social Indicators Research, 135(3), 869-878. Doi: 10.1007/s11205-016-1495-y
Blanco-Moreno, S., González-Fernández, A. M., & Muñoz-Gallego, P. A. (2024). Big data in tourism marketing: past research and future opportunities. Spanish journal of marketing-esic, 28(3), 266-286. Doi: 10.1108/SJME-06-2022-0134
Boulding, K. E. (1961). The image: Knowledge in life and society . University of Michigan press, 74, 36-50. URL: https://wedgeblade.net/files/archives_assets/20885.pdf
Bui, V., Alaei, A. R., Vu, H. Q., Li, G., & Law, R. (2022). Revisiting tourism destination image: A holistic measurement framework using big data. Journal of Travel Research, 61(6), 1287-1307. Doi: 10.1177/00472875211024749
Chi, C. G. Q., & Qu, H. (2008). Examining the structural relationships of destination image, tourist satisfaction and destination loyalty: An integrated approach. Tourism management, 29(4), 624-636. Doi: 10.1016/j.tourman.2007.06.007
Crompton, J. L. (1979). An assessment of the image of Mexico as a vacation destination and the influence of geographical location upon that image. Journal of travel research, 17(4), 18-23. Doi: 10.1177/004728757901700404
Dobni, D., & Zinkhan, G. M. (1990). In search of brand image: A foundation analysis. Advances in consumer research, 17(1), 110-134. URL: https://ijecm.co.uk/wp-content/uploads/2019/12/71210.pdf
Echtner, C. M., & Ritchie, J. B. (1991). The meaning and measurement of destination image. Journal of tourism studies, 2(2), 2-12. URL: https://www.academia.edu/download/24746223/jcudev_012855.pdf
Gallarza, M. G., Saura, I. G., & Garcı́a, H. C. (2002). Destination image: Towards a conceptual framework. Annals of tourism research, 29(1), 56-78. Doi:10.1016/S0160-7383(01)00031-7
Gartner, W. C. (1994). Image formation process. Journal of travel & tourism marketing, 2(2-3), 191-216. Doi: 10.1300/J073v02n02_12
Gross, R., & Acquisti, A. (2005). Information revelation and privacy in online social networks. Information revelation and privacy in online social networks conferences , 71-80. Doi: 10.1145/1102199.1102214
Gun, C. (1988). Vacationscape: Designing tourist regions. Van Nostrand Reinhold. New York: Van Nostrand Reinhold, 127, 54-106. URL: https://archive.org/details/vacationscapedes0000gunn_q6l4
He, H., & Singh, A. K. (2008, June). Graphs-at-a-time: query language and access methods for graph databases. Graphs-at-a-time: query language and access methods for graph databases, 405-418. Doi: 10.1145/1376616.1376660
Huang, J., Obracht-Prondzynska, H., Kamrowska-Zaluska, D., Sun, Y., & Li, L. (2021). The image of the City on social media: A comparative study using “Big Data” and “Small Data” methods in the Tri-City Region in Poland. Landscape and Urban Planning, 206, 103977. Doi: 10.1016/j.landurbplan.2020.103977
Huang, R. Y., Chang, W. J., & Chang, C. Y. (2021). Destination image analysis and its strategic implications: A literature review from 1990 to 2019. International Journal of Tourism & Hospitality Reviews, 8(1), 30-50. Doi: 10.18510/ijthr.2021.813
Hunt, J. (1975). Image as a Factor in Tourism Development. Journal of travel research, 3(13), 1-7. Doi: 10.1177/004728757501300301
Khan, A., Ashfaq, J., Bilal, M., Khan, M. H., & Shad, F. (2021). Destination image formation through User Generated Content (UGC). An updated literature review. Indian Journal of Economics and Business,  20(2), 1223-1238.
Kotler, P. (2002). Marketing places. Simon and Schuster, 77-132.
Ladhari, R., & Michaud, M. (2015). eWOM effects on hotel booking intentions, attitudes, trust, and website perceptions. International Journal of Hospitality Management, 46, 36-45. Doi: 10.1016/j.ijhm.2015.01.010
Lee, C., & Paik, I. (2017). Stock market analysis from Twitter and news based on streaming big data infrastructure. International Conference on Awareness Science and Technology (iCAST), 312-317. Doi: 10.1109/ICAwST.2017.8256469
Li, S., & Yang, B. (2022). Social media for landscape planning and design: a review and discussion. Landscape Research, 47(5), 648-663. Doi: 10.1080/01426397.2022.2060953
Lin, M. S., Liang, Y., Xue, J. X., Pan, B., & Schroeder, A. (2021). Destination image through social media analytics and survey method. International Journal of Contemporary Hospitality Management, 33(6), 2219-2238. Doi: 10.1108/IJCHM-08-2020-0861
Mahmoudi, Somayeh, Ranjbarian, Bahram Ranjbarian  &Fathi, Saeed. (2016). Developing a model of potential tourists' mental image of Iranian tourism destinations. Journal of Tourism Management Studies, 10(29), 21-44. (In persian) Doi: 20.1001.1.23223294.1394.10.29.2.9
Manovich, L. (2011). Trending: The promises and the challenges of big social data. Debates in the digital humanities, 2(1), 460-475. URL: https://www.academia.edu/download/35983875/64-article-2011_trending.pdf
Mansouri Moayed, Fereshteh & Soleimani, Samira. (2012). Marketing tools and tourists' mental image of the destination. Journal of Tourism Management Studies, 7(8), 93-110. (In persian) Doi: 20.1001.1.23223294.1391.7.18.5.9
Marchiori, E., & Önder, I. (2014). Reframing the image of a destination: A pre-post study on social media exposure. Information and Communication Technologies in Tourism, 335-347. Doi: 10.1007/978-3-319-14343-9_25
Marine-Roig, E., & Anton Clavé, S. (2016). A detailed method for destination image analysis using user-generated content. Information Technology & Tourism, 15(4), 341-364. Doi: 10.1007/s40558-015-0040-1
Martínez-Ruiz, M. P., Llodrá-Riera, I., & Jiménez-Zarco, A. I. (2018). Social media as information sources and their influence on the destination image: Opportunities for sustainability perception. sustainable tourism resources conference ,265-283. Doi: 10.4018/978-1-5225-5772-2.ch014
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007). Measurement and analysis of online social networks. Measurement and analysis of online social networks, 29-42. Doi: 10.1145/1298306.1298311
Moise, I. (2016, December). The technical hashtag in Twitter data: A hadoop experience. The technical hashtag in Twitter data: A hadoop experience, 3519-3528. Doi: 10.1109/BigData.2016.7841015
Phelps, A. (1986). Holiday destination image—the problem of assessment: An example developed in Menorca. Tourism management, 7(3), 168-180. Doi: 10.1016/0261-5177(86)90003-8
Picazo, P., & Moreno-Gil, S. (2019). Analysis of the projected image of tourism destinations on photographs: A literature review to prepare for the future. Journal of Vacation Marketing, 25(1), 3-24. Doi: 10.1177/1356766717736350
Pike, S. (2002). Destination image analysis—a review of 142 papers from 1973 to 2000. Tourism management, 23(5), 541-549. Doi: 10.1016/S0261-5177(02)00005-5
Rezaei Hajiabadi, J. , Mohammad shafiee, M. and kazemi, A. (2022). Developing a Model of Destination Value Co-creation by Focusing on the Role of the Tourist Personality Traits as a Moderator: Case Study of Selected Destinations in Iran. Journal of Tourism and Development, 11(1), 105-127. (In persian) Doi: 10.22034/jtd.2021.271077.2260
Stieglitz, S., & Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social network analysis and mining, 3(4), 1277-1291. Doi: 10.1007/s13278-012-0079-3
Stylidis, D., & Cherifi, B. (2018). Characteristics of destination image: visitors and non-visitors’ images of London. Tourism Review, 73(1), 55-67. Doi: 10.1108/TR-05-2017-0090
Sun, Y., Liang, C., & Chang, C. C. (2020). Online social construction of Taiwan's rural image: Comparison between Taiwanese self-representation and Chinese perception. Tourism Management, 76, 103968. Doi: 10.1016/j.tourman.2019.103968
Tan, W. K., & Wu, C. E. (2016). An investigation of the relationships among destination familiarity, destination image and future visit intention. Journal of destination marketing & management, 5(3), 214-226. Doi:  10.1016/j.jdmm.2015.12.008
Tasci, A. D., Gartner, W. C., & Tamer Cavusgil, S. (2007). Conceptualization and operationalization of destination image. Journal of hospitality & tourism research, 31(2), 194-223. Doi: 10.1177/1096348006297290
Tayebi, Amir and Zakavat, Kamran. (2018). A conceptual model for domestic tourists' mental image of Isfahan urban spaces. Journal of Tourism Management Studies, 13(43), 1-33. (in persian) Doi: 10.22054/tms.2018.9445
Wang, J., Li, Y., Wu, B., & Wang, Y. (2021). Tourism destination image based on tourism user generated content on internet. Tourism Review, 76(1), 125-137. Doi: 10.1108/TR-04-2019-0132
Xiang, Z., Schwartz, Z., Gerdes Jr, J. H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction?. International journal of hospitality management, 44, 120-130. Doi: 10.1016/j.ijhm.2014.10.013
Xiao, X., Fang, C., Lin, H., & Chen, J. (2022). A framework for quantitative analysis and differentiated marketing of tourism destination image based on visual content of photos. Tourism Management, 93, 104585. Doi: 10.1016/j.tourman.2022.104585
Zabelskyte, G., Kabisch, N., & Stasiskiene, Z. (2022). Patterns of urban green space use applying social media data: a systematic literature review. Land, 11(2), 238. Doi: 10.3390/land11020238
Zeng, B., & Gerritsen, R. (2014). What do we know about social media in tourism? A review. Tourism management perspectives, 10, 27-36. Doi: 10.1016/j.tmp.2014.01.001
Urban Planning Knowledge
Volume 9, Issue 2
2025
Pages 78-101

Files

History

  • Receive Date: 26 December 2024
  • Revise Date: 13 April 2025
  • Accept Date: 20 May 2025

Share

How to cite

Statistics