Abstract
In the tourism industry, the implementation of effective strategies to promote destinations is considered of utmost importance. Taking advantage of social media, Destination Management Organizations (DMOs) have embraced these platforms as direct channels of communication with potential visitors. However, it remains unclear to what extent these efforts work to effectively construct the desired image and influence visitors’ behavior. In order to explore this phenomenon, this study proposes a comparison of destination images within Instagram, used by both DMOs and visitors (user generated content). Thus, a deep-learning method is presented to automatically compute differences between destination images. Four destinations were selected from Mexico (two urban destinations and two beach destinations). The findings suggest that the images of urban destinations share more significant similarities, particularly in dimensions related to culture, tourist infrastructure, and natural resources when compared to beach destinations. Conversely, the images of beach destinations tend to converge on dimensions such as sun and sand, gastronomy, and entertainment, while differing in aspects related to tourist infrastructure and eco-tourism offerings. It is worth noting that these results underscore the importance of tailoring marketing strategies to the unique characteristics of each destination, taking into account the divergences and similarities in the perceptions of potential visitors.
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References
Álvarez-Carmona MÁ, Aranda R, Rodríguez-Gonzalez AY et al (2022) Natural language processing applied to tourism research: a systematic review and future research directions. J King Saud Univ Comput Inf Sci 34(10):10125–10144. https://doi.org/10.1016/j.jksuci.2022.10.010
Álvarez-Carmona MÁ, Díaz-Pacheco Á, Aranda R et al (2023) Overview of Rest-Mex at IberLEF 2023: research on sentiment analysis task for Mexican tourist texts. Proces Leng Nat 71:425–436
Arefieva V, Egger R, Yu J (2021) A machine learning approach to cluster destination image on Instagram. Tour Manag 85:104318. https://doi.org/10.1016/j.tourman.2021.104318
Baloglu S, McCleary KW (1999) A model of destination image formation. Ann Tour Res 26(4):868–897. https://doi.org/10.1016/s0160-7383(99)00030-4
Baumann F, Lopes MS, Lourenço P (2018a) Destination image through digital photography. Instagram as a data collector for UGC analysis. e-Rev Tour Res. https://ertr-ojs-tamu.tdl.org/ertr/index.php/ertr/article/view/117
Baumann F, Lopes MS, Lourenço P (2018b) Destination image through digital photography. Instagram as a data collector for UGC analysis. e-Rev Tour Res. https://ertr-ojs-tamu.tdl.org/ertr/article/view/117
Beerli A, Martín JD (2004) Factors influencing destination image. Ann Tour Res 31(3):657–681. https://doi.org/10.1016/j.annals.2004.01.010
Bernkopf D, Nixon L (2018) The impact of visual social media on the projected image of a destination: the case of Mexico City on Instagram. In: Information and communication technologies in tourism 2019. Springer International Publishing, Basel, pp 145–157. https://doi.org/10.1007/978-3-030-05940-8_12
Blanco-Moreno S, González-Fernández AM, Muñoz-Gallego PA (2023) Big data in tourism marketing: past research and future opportunities. Span J Mark ESIC. https://doi.org/10.1108/sjme-06-2022-0134
Bramwell B, Rawding L (1996) Tourism marketing images of industrial cities. Ann Tour Res 23(1):201–221. https://doi.org/10.1016/0160-7383(95)00061-5
Carvache-Franco M, Solis-Radilla MM, Carvache-Franco W et al (2022) The cognitive image and behavioral loyalty of a coastal and marine destination: a study in Acapulco, Mexico. J Qual Assur Hosp Tour. https://doi.org/10.1080/1528008x.2021.2024780
Chan CS, Zhang Y (2018) Matching projected image with perceived image for geotourism development: a qualitative-quantitative integration. Asian Geogr 35(2):143–160. https://doi.org/10.1080/10225706.2018.1527235
Choi S, Lehto XY, Morrison AM (2007) Destination image representation on the web: content analysis of Macau travel related websites. Tour Manag 28(1):118–129
Couret C, Peñarroya M (2016) Curso internacional de la omt en argentina excelencia en la gestión turística: El turista digital
Cuomo MT, Tortora D, Foroudi P et al (2021) Digital transformation and tourist experience co-design: big social data for planning cultural tourism. Technol Forecast Soc Change 162:120345. https://doi.org/10.1016/j.techfore.2020.120345
del Mar Gálvez-Rodríguez M, Alonso-Cañadas J, de Rosario AH et al (2020) Exploring best practices for online engagement via Facebook with local destination management organisations (DMOs) in europe: A longitudinal analysis. Tour Manag Perspect 34:100636. https://doi.org/10.1016/j.tmp.2020.100636
Deng N, Liu J, Dai Y et al (2019) Different cultures, different photos: a comparison of Shanghai’s pictorial destination image between East and West. Tour Manag Perspect 30:182–192. https://doi.org/10.1016/j.tmp.2019.02.016
Díaz-Pacheco A, Aranda R (2022) El turismo en la era de los macrodatos. “Cómo ves” 289:28–31
Diaz-Pacheco A, Álvarez-Carmona MÁ, Guerrero-Rodríguez R et al (2022) Artificial intelligence methods to support the research of destination image in tourism. a systematic review. J Exp Theor Artif Intell. https://doi.org/10.1080/0952813x.2022.2153276
Díaz-Pacheco Á, Álvarez-Carmona MÁ, Rodríguez-González AY et al (2023a) Measuring the difference between pictures from controlled and uncontrolled sources to promote a destination. a deep learning approach. Int J Interact Multimed Artif Intell. https://doi.org/10.9781/ijimai.2023.10.003
Díaz-Pacheco Á, Guerrero-Rodríguez R, Álvarez-Carmona MÁ et al (2023b) A comprehensive deep learning approach for topic discovering and sentiment analysis of textual information in tourism. J King Saud Univ Comput Inf Sci 35(9):101746. https://doi.org/10.1016/j.jksuci.2023.101746
Egger R, Yu J (2021) Identifying hidden semantic structures in Instagram data: a topic modelling comparison. Tour Rev. https://doi.org/10.1108/tr-05-2021-0244
Egger R, Gumus O, Kaiumova E et al (2022) Destination image of DMO and UGC on Instagram: a machine-learning approach. In: Stienmetz JL, Ferrer-Rosell B, Massimo D (eds) Information and communication technologies in tourism 2022. Springer International Publishing, Cham, pp 343–355. https://doi.org/10.1007/978-3-030-94751-4_31
Ensafi F (2023) Investigating the effect of social networks on the formation of the destination image and loyalty to the tourist destination (case study: tourists of larnaca, cyprus). Res J Manag Rev 8(1):1–6
Feizollah A, Mostafa MM, Sulaiman A et al (2021) Exploring halal tourism tweets on social media. J Big Data 8(1). https://doi.org/10.1186/s40537-021-00463-5
Fellbaum C, Miller G (1998) WordNet: an electronic lexical database. MIT Press, Cambridge. https://ieeexplore.ieee.org/book/6267389
Frías DM, Rodríguez MA, Castañeda JA (2008) Internet vs. travel agencies on pre-visit destination image formation: an information processing view. Tour Manag 29(1):163–179. https://doi.org/10.1016/j.tourman.2007.02.020
Gallarza MG, Saura IG, García HC (2002) Destination image: towards a conceptual framework. Ann Tour Res 29(1):56–78. https://doi.org/10.1016/s0160-7383(01)00031-7
Gartner WC (1994) Image formation process. J Travel Tour Mark 2(2–3):191–216. https://doi.org/10.1300/j073v02n02_12
González-Rodríguez MR, Martínez-Torres R, Toral S (2016) Post-visit and pre-visit tourist destination image through eWOM sentiment analysis and perceived helpfulness. International Journal of Contemporary Hospitality Management 28(11):2609–2627. https://doi.org/10.1108/ijchm-02-2015-0057
González-Uribe EG, Mayorga-Salamanca PI, Gutiérrez-Sáchez PV (2021) La contaminación ambiental como factor de influencia en el wom: caso estudiantes extranjeros y nacionales en guadalajara, méxico. Red Int Investig Compet 15:1508–1528
Gorji AS, Almeida-García F, Melé PM (2021) Analysis of the projected image of tourism destinations on photographs: the case of Iran on Instagram. Anatolia. https://doi.org/10.1080/13032917.2021.2001665
Govers R, Go FM (2003) Deconstructing destination image in the information age. Inf Technol Tour 6(1):13–29. https://doi.org/10.3727/109830503108751199
Grosspietsch M (2006) Perceived and projected images of Rwanda: visitor and international tour operator perspectives. Tour Manag 27(2):225–234. https://doi.org/10.1016/j.tourman.2004.08.005
Gunn CA (1997) Vacationscape. Taylor & Francis, Philadelphia
Hauser D, Leopold A, Egger R et al (2022) Aesthetic perception analysis of destination pictures using #beautifuldestinations on Instagram. J Destin Mark Manag 24:100702. https://doi.org/10.1016/j.jdmm.2022.100702
Huang X, Han Y, Meng Q et al (2022) Do the DMO and the tourists deliver the similar image? Research on representation of the health destination image based on UGC and the theory of discourse power: a case study of Bama, China. Sustainability 14(2):953. https://doi.org/10.3390/su14020953
Huang GI, Karl M, Wong IA et al (2023) Tourism destination research from 2000 to 2020: a systematic narrative review in conjunction with bibliographic mapping analysis. Tour Manag 95:104686. https://doi.org/10.1016/j.tourman.2022.104686
Iglesias-Sánchez PP, Correia MB, Jambrino-Maldonado C et al (2020) Instagram as a co-creation space for tourist destination image-building: Algarve and Costa del Sol case studies. Sustainability 12(7):2793. https://doi.org/10.3390/su12072793
Inversini A, Eynard D, Marchiori E et al (2012) Destinations similarity based on user generated pictures’ tags. In: Information and communication technologies in tourism 2012. Springer Vienna, Helsingborg, pp 483–493. https://doi.org/10.1007/978-3-7091-1142-0_42
Jiménez-Barreto J, Rubio N, Campo S et al (2020) Linking the online destination brand experience and brand credibility with tourists’ behavioral intentions toward a destination. Tour Manag 79:104101. https://doi.org/10.1016/j.tourman.2020.104101
Khoa BT, Ly NM, Uyen VTT et al (2021) The impact of social media marketing on the travel intention of z travelers. In: 2021 IEEE international IOT, electronics and mechatronics conference (IEMTRONICS). IEEE, Toronto. https://doi.org/10.1109/iemtronics52119.2021.9422610
Kolodziej M, Tarnowski P, Sawicki DJ et al (2020) Fatigue detection caused by office work with the use of EOG signal. IEEE Sens J 20(24):15213–15223. https://doi.org/10.1109/jsen.2020.3012404
Kotler PT, Bowen JT, Makens JC et al (2016) Marketing for hospitality and tourism, 7th edn. Pearson, Upper Saddle River
Kuhzady S, Ghasemi V (2019) Pictorial analysis of the projected destination image: Portugal on Instagram. Tour Anal 24(1):43–54. https://doi.org/10.3727/108354219x15458295631954
Lalicic L, Huertas A, Moreno A et al (2020) Emotional brand communication on Facebook and twitter: are DMOs successful? J Destin Mark Manag 16:100350. https://doi.org/10.1016/j.jdmm.2019.03.004
Langner R, Steinborn MB, Chatterjee A et al (2010) Mental fatigue and temporal preparation in simple reaction-time performance. Acta Psychol 133(1):64–72. https://doi.org/10.1016/j.actpsy.2009.10.001
Lee E, Chung N, Koo C (2023) Exploring touristic experiences on destination image modification. Tour Manag Perspect 47:101114. https://doi.org/10.1016/j.tmp.2023.101114
Li J, Ali F, Kim W (2015) Reexamination of the role of destination image in tourism: an updated literature review. e-Rev Tour Res 12:191–209. https://discovery.ebsco.com/c/ylm4lv/details/ew7wvjdndv
Liu MT, Liu Y, Mo Z et al (2020) Using text mining to track changes in travel destination image: the case of Macau. Asia Pac J Mark Logist 33(2):371–393. https://doi.org/10.1108/APJML-08-2019-0477
Lojo A, Li M, Xu H (2020) Online tourism destination image: components, information sources, and incongruence. J Travel Tour Mark 37(4):495–509. https://doi.org/10.1080/10548408.2020.1785370
Mak AH (2017) Online destination image: comparing national tourism organisation’s and tourists’ perspectives. Tour Manag 60:280–297. https://doi.org/10.1016/j.tourman.2016.12.012
Marine-Roig (2019) Destination image analytics through traveller-generated content. Sustainability 11(12):3392. https://doi.org/10.3390/su11123392
Marine-Roig E, Ferrer-Rosell B (2018) Measuring the gap between projected and perceived destination images of Catalonia using compositional analysis. Tour Manag 68:236–249. https://doi.org/10.1016/j.tourman.2018.03.020
Matos N, Mendes J, Valle P (2017) Revisiting the destination image construct through a conceptual model. Dos Algarves Tour Hosp Manag J 21:101–117
Nixon LJ (2019) An online image annotation service for destination image measurement. e-Rev Tour Res 17(2). https://journals.tdl.org/ertr/index.php/ertr/article/view/506
Nixon L (2022) Do DMOs promote the right aspects of the destination? A study of Instagram photography with a visual classifier. In: Stienmetz JL, Ferrer-Rosell B, Massimo D (eds) Information and Communication Technologies in Tourism 2022. Springer International Publishing, Cham, pp 174–186
Nixon L (2023) How do destinations relate to one another? a study of destination visual branding on Instagram. In: Information and communication technologies in tourism 2023. Springer Nature Switzerland, pp 204–216. https://doi.org/10.1007/978-3-031-25752-0_23
Nixon L, Popova A, Onder I (2017) How Instagram influences visual destination image—a case study of Jordan and Costa Rica. In: ENTER2017 eTourism conference
Olmos FG (2021) Los mexicanos usan más de 10 redes sociales al día. https://expansion.mx/tecnologia/2021/06/30/los-mexicanos-usan-mas-de-10-redes-sociales-al-dia
Padilla R, Netto SL, da Silva EAB (2020) A survey on performance metrics for object-detection algorithms. In: 2020 International conference on systems, signals and image processing (IWSSIP). IEEE, Niterói. https://doi.org/10.1109/iwssip48289.2020.9145130
Palau-Saumell R, Forgas-Coll S, Amaya-Molinar CM et al (2015) Examining how country image influences destination image in a behavioral intentions model: the cases of Lloret de Mar (Spain) and Cancun (Mexico). J Travel Tour Mark 33(7):949–965. https://doi.org/10.1080/10548408.2015.1075456
Park H, Liang Y, Ying Q (2021) Projected and perceived destination image on social media: comparing perception of destination image between official DMO in South Korea and Chinese travelers. In: The China Tourism Forum 2019. https://static1.squarespace.com/static/5af455a471069901dd1286bc/t/5dbc8c5340d9ee7da988cc90/1572637779809/Projected+and+Perceived+Destination+Image+on+Social+Media_+Compar.pdf
Pedersen T, Patwardhan S, Michelizzi J et al (2004) Wordnet: similarity-measuring the relatedness of concepts. In: AAAI, pp 25–29. https://www.aaai.org/Papers/AAAI/2004/AAAI04-160.pdf
Sanchez SA, Romero HJ, Morales AD (2020) A review: comparison of performance metrics of pretrained models for object detection using the TensorFlow framework. IOP Conf Ser Mater Sci Eng 844:012024. https://doi.org/10.1088/1757-899x/844/1/012024
SECTUR (2021) Compendio estadístico del turismo en méxico 2020. https://datatur.sectur.gob.mx/SitePages/CompendioEstadistico.aspx
Sewak M, Karim M, Pujari P (2018) Practical convolutional neural networks: implement advanced deep learning models using Python. Packt Publishing, Birmingham. https://books.google.com.mx/books?id=bOlODwAAQBAJ
Shankar V, Dave A, Roelofs R et al (2021) Do image classifiers generalize across time? In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV), pp 9661–9669. https://openaccess.thecvf.com/content/ICCV2021/html/Shankar_Do_Image_Classifiers_Generalize_Across_Time_ICCV_2021_paper.html
Song H, Hsu C (2013) The image of Taiwan as a travel destination: perspectives from mainland China. J Travel Tour Mark 30(3):253–271. https://doi.org/10.1080/10548408.2013.774919
Song SG, Kim DY (2016) A pictorial analysis of destination images on Pinterest: the case of Tokyo, Kyoto, and Osaka, Japan. J Travel Tour Mark 33(5):687–701. https://doi.org/10.1080/10548408.2016.1167384
Song S, Park SB, Park K (2021) Thematic analysis of destination images for social media engagement marketing. Ind Manag Data Syst 121(6, SI):1375–1397. https://doi.org/10.1108/IMDS-12-2019-0667
Stepchenkova S, Zhan F (2013) Visual destination images of Peru: comparative content analysis of DMO and user-generated photography. Tour Manag 36:590–601. https://doi.org/10.1016/j.tourman.2012.08.006
Sun G (2020) Symmetry analysis in analyzing cognitive and emotional attitudes for tourism consumers by applying artificial intelligence python technology. Symmetry 12(4):606. https://doi.org/10.3390/sym12040606
Tang Y, Uz LH, Cai Y et al (2013) Earth mover’s distance based similarity search at scale. Proc VLDB Endow 7(4):313–324. https://doi.org/10.14778/2732240.2732249
Toumeh B (2022) Exploitation of digital transformation technologies in smart tourism destinations: facts and challenges, vol 1. Litres, Moscow, pp 124–127
Tugores-Ques J, Bonilla-Quijada M (2022) A touristic tale of four cities on Instagram. J Vacat Mark 29(1):54–70. https://doi.org/10.1177/13567667221078246
Tussyadiah I (2010) Destination-promoted and visitor-generated images—do they represent similar stories? CABI, Wallingford. https://doi.org/10.1079/9781845936112.0156
UNWTO (2022) UNWTO world tourism barometer. https://doi.org/10.18111/wtobarometereng
Wang R, Luo J, Huang SS (2020) Developing an artificial intelligence framework for online destination image photos identification. J Destin Mark Manag 18:100512. https://doi.org/10.1016/j.jdmm.2020.100512
Wu Z, Palmer M (1994) Verb semantics and lexical selection. https://doi.org/10.48550/arXiv.cmp-lg/9406033. arXiv:cmp-lg/9406033
Xiao X, Fang C, Lin H et al (2022) A framework for quantitative analysis and differentiated marketing of tourism destination image based on visual content of photos. Tour Manag 93:104585. https://doi.org/10.1016/j.tourman.2022.104585
Yang S, Liu Y, Wu X (2021) 1 + 1 \(<\) 2! effects of social media engagement and advertising on firm value of tourism and hospitality companies. J Hosp Tour Res 45(8):1417–1439. https://doi.org/10.1177/10963480211015361
Yu J, Egger R (2021) Color and engagement in touristic Instagram pictures: a machine learning approach. Ann Tour Res 89:103204. https://doi.org/10.1016/j.annals.2021.103204
Yuan Y, Mou L, Lu X (2015) Scene recognition by manifold regularized deep learning architecture. IEEE Trans Neural Netw Learn Syst 26(10):2222–2233. https://doi.org/10.1109/tnnls.2014.2359471
Zhang K, Chen Y, Lin Z (2020) Mapping destination images and behavioral patterns from user-generated photos: a computer vision approach. Asia Pac J Tour Res 25(11):1199–1214. https://doi.org/10.1080/10941665.2020.1838586
Zhou B, Lapedriza A, Khosla A et al (2018) Places: a 10 million image database for scene recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1452–1464. https://doi.org/10.1109/tpami.2017.2723009
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Díaz-Pacheco, Á., Guerrero-Rodríguez, R., Álvarez-Carmona, M.Á. et al. Quantifying differences between UGC and DMO’s image content on Instagram using deep learning. Inf Technol Tourism (2024). https://doi.org/10.1007/s40558-023-00282-9
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DOI: https://doi.org/10.1007/s40558-023-00282-9