IDEAS home Printed from https://ideas.repec.org/a/spr/infott/v26y2024i3d10.1007_s40558-024-00295-y.html
   My bibliography  Save this article

Automated photo filtering for tourism domain using deep and active learning: the case of Israeli and worldwide cities on instagram

Author

Listed:
  • Abigail Paradise-Vit

    (The Max Stern Yezreel Valley College)

  • Aviad Elyashar

    (Shamoon College of Engineering (SCE))

  • Yarden Aronson

    (The Max Stern Yezreel Valley College)

Abstract

Social media platforms like Instagram significantly influence tourists’ travel decisions by providing them with valuable insights, recommendations, authentic information, and points of interest. However, photos shared with location-specific hashtags, even those related to tourist attractions, do not always reflect the actual destination, creating challenges for potential visitors seeking accurate information. To assist tourists in finding pertinent tourism information for specific destinations, we propose VISTA: Visual Identification of Significant Travel Attractions. The proposed method employs deep learning and active learning techniques to automatically classify photos into: ‘Tourism-Related’ photos (i.e., photos related to tourism) and ‘Non-Tourism-Related’ photos (i.e., photos unrelated to tourism). To train our machine learning classifier, we created a dataset containing photos of the 10 most popular Israeli cities on Instagram. The classifier obtained an accuracy score of 0.965 and a weighted F1 score of 0.964. Evaluating our classifier’s global generalization on the InstaCities100K dataset, derived from InstaCities1M, yielded an accuracy score of 0.958 and a weighted F1 score of 0.959. The effectiveness of VISTA was demonstrated by comparing tourism-related and non-tourism-related photos in terms of photo proportion, user engagement, and object comparison. We found that most photos published on Instagram associated with cities are irrelevant to tourists and that tourism-related photos received more likes than non-tourism-related photos. Finally, there was a low overlap between objects in the two photo collections. Based on these results, we conclude that VISTA can help tourists tackle the problem of finding relevant tourism-related photos among the high volume of photos available on Instagram.

Suggested Citation

  • Abigail Paradise-Vit & Aviad Elyashar & Yarden Aronson, 2024. "Automated photo filtering for tourism domain using deep and active learning: the case of Israeli and worldwide cities on instagram," Information Technology & Tourism, Springer, vol. 26(3), pages 553-582, September.
    • Handle: RePEc:spr:infott:v:26:y:2024:i:3:d:10.1007_s40558-024-00295-y
    DOI: 10.1007/s40558-024-00295-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40558-024-00295-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40558-024-00295-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Huai, Songyao & Chen, Fen & Liu, Song & Canters, Frank & Van de Voorde, Tim, 2022. "Using social media photos and computer vision to assess cultural ecosystem services and landscape features in urban parks," Ecosystem Services, Elsevier, vol. 57(C).
    2. Estela Marine-Roig, 2019. "Destination Image Analytics Through Traveller-Generated Content," Sustainability, MDPI, vol. 11(12), pages 1-23, June.
    3. Ahmed Derdouri & Toshihiro Osaragi, 2021. "A machine learning-based approach for classifying tourists and locals using geotagged photos: the case of Tokyo," Information Technology & Tourism, Springer, vol. 23(4), pages 575-609, December.
    4. Schirpke, Uta & Tasser, Erich & Ebner, Manuel & Tappeiner, Ulrike, 2021. "What can geotagged photographs tell us about cultural ecosystem services of lakes?," Ecosystem Services, Elsevier, vol. 51(C).
    5. Diletta Acuti & Valentina Mazzoli & Raffaele Donvito & Priscilla Chan, 2018. "An instagram content analysis for city branding in London and Florence," Journal of Global Fashion Marketing, Taylor & Francis Journals, vol. 9(3), pages 185-204, July.
    6. Richards, Daniel R. & Tunçer, Bige, 2018. "Using image recognition to automate assessment of cultural ecosystem services from social media photographs," Ecosystem Services, Elsevier, vol. 31(PC), pages 318-325.
    7. John Fotis & Dimitrios Buhalis & Nicos Rossides, 2011. "Social Media Impact on Holiday Travel Planning: The Case of the Russian and the FSU Markets," International Journal of Online Marketing (IJOM), IGI Global Scientific Publishing, vol. 1(4), pages 1-19, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nie, Xin & Huang, Chengdao & Wang, Han, 2025. "A new method to classify cultural ecosystem services and visualize their economic value: A case study of Guilin, a famous tourist destination in China," Ecosystem Services, Elsevier, vol. 72(C).
    2. Schirpke, Uta & Ghermandi, Andrea & Sinclair, Michael & Van Berkel, Derek & Fox, Nathan & Vargas, Leonardo & Willemen, Louise, 2023. "Emerging technologies for assessing ecosystem services: A synthesis of opportunities and challenges," Ecosystem Services, Elsevier, vol. 63(C).
    3. Huai, Songyao & Chen, Fen & Liu, Song & Canters, Frank & Van de Voorde, Tim, 2022. "Using social media photos and computer vision to assess cultural ecosystem services and landscape features in urban parks," Ecosystem Services, Elsevier, vol. 57(C).
    4. Busch, Christin & Specht, Kathrin & Inostroza, Luis & Falke, Matthias & Zepp, Harald, 2024. "Disentangling cultural ecosystem services co-production in urban green spaces through social media reviews," Ecosystem Services, Elsevier, vol. 70(C).
    5. Marc Vallverdu-Gordi & Estela Marine-Roig, 2023. "The Role of Graphic Design Semiotics in Environmental Awareness Campaigns," IJERPH, MDPI, vol. 20(5), pages 1-19, February.
    6. Ángel Díaz-Pacheco & Rafael Guerrero-Rodríguez & Miguel Á. Álvarez-Carmona & Ansel Y. Rodríguez-González & Ramón Aranda, 2024. "Quantifying differences between UGC and DMO’s image content on Instagram using deep learning," Information Technology & Tourism, Springer, vol. 26(2), pages 293-329, June.
    7. Nadir Al Naqbi & Naill Al Momani & Amanda Davies, 2022. "The Influence of Social Media on Perceived Levels of National Security and Crisis: A Case Study of Youth in the United Arab Emirates," Sustainability, MDPI, vol. 14(17), pages 1-19, August.
    8. Kaiser, Nina N. & Ghermandi, Andrea & Feld, Christian K. & Hershkovitz, Yaron & Palt, Martin & Stoll, Stefan, 2021. "Societal benefits of river restoration – Implications from social media analysis," Ecosystem Services, Elsevier, vol. 50(C).
    9. Dickinson, Janet E. & Hibbert, Julia F. & Filimonau, Viachaslau, 2016. "Mobile technology and the tourist experience: (Dis)connection at the campsite," Tourism Management, Elsevier, vol. 57(C), pages 193-201.
    10. Jinwen Tang & Jinlin Cheng & Min Zhang, 2024. "Forecasting Airbnb prices through machine learning," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(1), pages 148-160, January.
    11. Richards, Daniel Rex & Lavorel, Sandra, 2022. "Integrating social media data and machine learning to analyse scenarios of landscape appreciation," Ecosystem Services, Elsevier, vol. 55(C).
    12. Márquez, Laura Andreina Matos & Rezende, Eva Caroline Nunes & Machado, Karine Borges & Nascimento, Emilly Layne Martins do & Castro, Joana D'arc Bardella & Nabout, João Carlos, 2023. "Trends in valuation approaches for cultural ecosystem services: A systematic literature review," Ecosystem Services, Elsevier, vol. 64(C).
    13. Xiliang Chen & Gang Li & Muhammad Sajid Mehmood & Qifan Nie & Jie Yu, 2023. "Integration and differentiation: comparison of photography behaviors using unmanned aerial vehicle data in China and Europe," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    14. Ruochen Ma & Katsunori Furuya, 2024. "Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review," Land, MDPI, vol. 13(2), pages 1-22, February.
    15. Jooyoung Hwang & Anita Eves & Jason L. Stienmetz, 2021. "The Impact of Social Media Use on Consumers’ Restaurant Consumption Experiences: A Qualitative Study," Sustainability, MDPI, vol. 13(12), pages 1-33, June.
    16. Beata Jolanta Gierczak-Korzeniowska & Mateusz Stopa & Krzysztof Szpara, 2022. "Methodology of Cyclical Research of the Tourist Image of Podkarpackie Province (Poland)—A Proposal of a Solution and Selected Research Results," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    17. Manley, Kyle & Nyelele, Charity & Egoh, Benis N., 2022. "A review of machine learning and big data applications in addressing ecosystem service research gaps," Ecosystem Services, Elsevier, vol. 57(C).
    18. Yuanting Yang & Wei Duan, 2024. "An Interpretation of Landscape Preferences Based on Geographic and Social Media Data to Understand Different Cultural Ecosystem Services," Land, MDPI, vol. 13(2), pages 1-23, January.
    19. Lavorel, Sandra & Rey, Pierre-Louis & Grigulis, Karl & Zawada, Mégane & Byczek, Coline, 2020. "Interactions between outdoor recreation and iconic terrestrial vertebrates in two French alpine national parks," Ecosystem Services, Elsevier, vol. 45(C).
    20. Do-Hyung Park & Sungwook Lee, 2021. "UGC Sharing Motives and Their Effects on UGC Sharing Intention from Quantitative and Qualitative Perspectives: Focusing on Content Creators in South Korea," Sustainability, MDPI, vol. 13(17), pages 1-13, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infott:v:26:y:2024:i:3:d:10.1007_s40558-024-00295-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.