Special Issue Call for Papers
AI and Big Data in Tourism
Guest Editor:
Andrei Kirilenko, Department of Tourism, Hospitality & Event Management, University of Florida
Introduction
Over the past decade, the AI and Big Data revolution has brought new possibilities for decision-making and innovation based on novel data analysis methods. Tourism geographies is well-positioned to benefit from these ongoing transformations in terms of new data, innovative data analysis methodologies, and emerging applications. The multidimensional data derived from tourists’ digital activities, images, social media narratives, movement trajectories, as well as lab-based experimental data such as sensor-based physiological responses, present untapped opportunities as well as methodological challenges for tourism researchers.
While the field is highly fragmented, and the methods to analyze data are still evolving, tourism scholars already engage with a range of AI and Big Data methods:
While the field is highly fragmented, and the methods to analyze data are still evolving, tourism scholars already engage with a range of AI and Big Data methods:
- Large Language Models (ChatGPT, Gemini, BERT, LLAMA, etc.) used in academic research, decision-making, and tourism applications (Kirilenko & Stepchenkova, 2025; Stergiou & Nella, 2024)
- Advanced analysis of social media, such as customer reviews, blogs, vlogs, and other examples of tracking tourists’ digital footprint. The methodologies include network analysis, text mining, image recognition, analysis of soundscapes and smellscapes, and many others (Cheng, 2024; Dann & Jacobsen, 2003; Kirilenko et al., 2021).
- Spatial data analysis and visualization with GIS: overtourism, hot spot analysis, mapping of tourist routes, travel photo locations, and mapping of digital traces (Chhetri & Arrowsmith, 2008; Hardy, A., & Shoval, N., 2025; Kirilenko et al., 2021; Kirilenko et al., 2023; Minasi et al., 2023; Morales-Pérez et al., 2022, Su et al., 2019; Zhang et al., 2022).
- Virtual travel (VR tourism), especially in combination with physiological data such as heartbeat, galvanic skin response, pupil dilation, and face expression (Beck et al., 2019; Dewailly, 1999; Nhan et al., 2025).
This Special Issue bridges ongoing research in tourism studies with tourism geographies scholarship focused on core questions of space, place and environment. It seeks papers that are methodologically innovative and critically reflect on the possibilities and challenges of integrating new data sources and analytic techniques into tourism geographies. It is particularly interested in papers focused on Large Language Models, data mining, text mining, image recognition, user-generated content, GIS analysis, and similar applications outlined above.
Rationale and Audience
This special issue will chart new pathways for tourism geographers to better understand core questions in the field such as tourism representations, overtourism, tourism growth and degrowth, labor mobilities, disaster resilience, destination development, environmental change. The Special Issue will be of broad interest to tourism scholars working in tourism geographies, digital geographies, geospatial analysis of tourism, as well as tourism marketing and destination development. The Issue will deliver a curated set of papers covering both empirical findings and critical debates that advance the methodological, theoretical, and practical understanding of the field.
The Special Issue engages a wide range of scholars across tourism geography, digital humanities, and GIS analysis, thereby increasing the visibility and citation potential of the issue. The convergence of artificial intelligence, big data, and tourism geography is a rapidly evolving and highly relevant field. As both community stakeholders and industry increasingly adopt data-driven solutions, the academic inquiry into these practices becomes more important.
The Special Issue engages a wide range of scholars across tourism geography, digital humanities, and GIS analysis, thereby increasing the visibility and citation potential of the issue. The convergence of artificial intelligence, big data, and tourism geography is a rapidly evolving and highly relevant field. As both community stakeholders and industry increasingly adopt data-driven solutions, the academic inquiry into these practices becomes more important.
The Scope
This Special Issue proposal targets innovative, advanced, data-intensive research in tourism with particular interest in Large Language models, data mining, text mining, image recognition, user-generated content, GIS analysis, and similar applications.
Specifically, the Special Issue invites contributions dealing with but not limited to the following themes:
Specifically, the Special Issue invites contributions dealing with but not limited to the following themes:
- Integration of large language models (LLMs) in tourism research methodology;
- AI applications in destination image analysis, chatbot communication, and automated itinerary design;
- Social media analytics for understanding tourist behavior, sentiment, crisis responses including the overtourism crises, etc.
- Geospatial tourism data modeling, including spatiotemporal clustering, GPS trajectory analysis, travel monitoring, and volunteered geospatial data with applications in tourism research, planning, and governance.
- Immersive technologies and biometric sensing, especially studies integrating VR/AR environments with biometric feedback to assess tourist emotions, preferences, and embodied experiences.
Abstract Submission
Extended abstracts (approximately 1300 words, including references and a minimum of 4 keywords) should be sent to the Guest Editor Andrei Kirilenko ([email protected]) by July 30 2025. Affiliations, contact information (including email and ORCID) should also be included in the abstract.
Authors will be informed of the outcome of the abstract review by August 31 2025. Those invited to submit their contributions will have to submit the full paper by no later than February 28 2026. The manuscripts will be subject to the usual peer review process of Tourism Geographies.
Authors will be informed of the outcome of the abstract review by August 31 2025. Those invited to submit their contributions will have to submit the full paper by no later than February 28 2026. The manuscripts will be subject to the usual peer review process of Tourism Geographies.
Timeline
Extended abstract submission: July 30 2025
Notification on extended abstracts: August 31 2025
Pre-submission review to guest editors: January 31 2026
Submission of full paper to the journal: February 28 2026
Notification on extended abstracts: August 31 2025
Pre-submission review to guest editors: January 31 2026
Submission of full paper to the journal: February 28 2026
Guest Editor’s Bio
Andrei Kirilenko has a degree in Applied Mathematics and a PhD in Computer Science. He started his career developing computer models for evaluating climate change impacts on the natural environment and agriculture. Gradually, his research and teaching interests have shifted towards quantitative social science research. Since 2015, he has held the position of Associate Professor at the Department of Tourism, Hospitality, and Event Management at the University of Florida teaching courses on Data mining for social sciences, Geographical Information Systems, Statistics, and Research methods. Dr. Kirilenko has served as an expert in the global assessments of the Intergovernmental Panel on Climate Change (IPCC) and the United Nations Environmental Program (UNEP). His research is centered on AI and Big Data analysis in tourism, which is also a topic of his textbook “Practical Data Mining with AI for Social Scientists” (Springer, 2025).
References
Beck, J., Rainoldi, M., & Egger, R. (2019). Virtual reality in tourism: a state-of-the-art review. Tourism review, 74(3), 586-612.
Cheng, M. (2024). Social media and tourism geographies: Mapping future research agenda. Tourism Geographies, 1-10.
Chhetri, P., & Arrowsmith, C. (2008). GIS-based modelling of recreational potential of nature-based tourist destinations. Tourism Geographies, 10(2), 233-257.
Dann, G., & Jacobsen, J. K. S. (2003). Tourism smellscapes. Tourism Geographies, 5(1), 3-25.
Dewailly, J. M. (1999). Sustainable tourist space: from reality to virtual reality?. Tourism Geographies, 1(1), 41-55.
Hardy, A., & Shoval, N. (2025). 25 years of tourist tracking: a geographical perspective. Tourism Geographies, 1-12.
Kirilenko, A. P., Ma, S., Stepchenkova, S. O., Su, L., & Waddell, T. F. (2023). Detecting early signs of overtourism: bringing together indicators of tourism development with data fusion. Journal of Travel Research, 62(2), 382-398.
Kirilenko, A. P., & Stepchenkova, S. (2025). Facilitating topic modeling in tourism research: Comprehensive comparison of new AI technologies. Tourism Management, 106, 105007.
Kirilenko, A. P., Stepchenkova, S. O., & Dai, X. (2021). Automated topic modeling of tourist reviews: does the Anna Karenina principle apply?. Tourism Management, 83, 104241.
Minasi, S. M., Lohmann, G., & Valduga, V. (2023). Geographic Information Systems are critical tools to manage wine tourism regions. Tourism Geographies, 25(1), 198-219.
Morales-Pérez, S., Garay, L., & Wilson, J. (2022). Airbnb’s contribution to socio-spatial inequalities and geographies of resistance in Barcelona. Tourism Geographies, 24(6-7), 978-1001.
Nhan, M., Kralj, A., Moyle, B., & Liu, B. (2025). Physiological measurement techniques in virtual tourism research: three caveats for future studies. Tourism Recreation Research, 1-9.
Stergiou, D. P., & Nella, A. (2024). ChatGPT and Tourist Decision‐Making: An Accessibility–Diagnosticity Theory Perspective. International Journal of Tourism Research, 26(5), e2757.
Su, L., Stepchenkova, S., & Kirilenko, A. P. (2019). Online public response to a service failure incident: Implications for crisis communications. Tourism Management, 73, 1-12.
Zhang, H., Huang, R., Zhang, Y., & Buhalis, D. (2022). Cultural ecosystem services evaluation using geolocated social media data: A review. Tourism Geographies, 24(4-5), 646-668.
Cheng, M. (2024). Social media and tourism geographies: Mapping future research agenda. Tourism Geographies, 1-10.
Chhetri, P., & Arrowsmith, C. (2008). GIS-based modelling of recreational potential of nature-based tourist destinations. Tourism Geographies, 10(2), 233-257.
Dann, G., & Jacobsen, J. K. S. (2003). Tourism smellscapes. Tourism Geographies, 5(1), 3-25.
Dewailly, J. M. (1999). Sustainable tourist space: from reality to virtual reality?. Tourism Geographies, 1(1), 41-55.
Hardy, A., & Shoval, N. (2025). 25 years of tourist tracking: a geographical perspective. Tourism Geographies, 1-12.
Kirilenko, A. P., Ma, S., Stepchenkova, S. O., Su, L., & Waddell, T. F. (2023). Detecting early signs of overtourism: bringing together indicators of tourism development with data fusion. Journal of Travel Research, 62(2), 382-398.
Kirilenko, A. P., & Stepchenkova, S. (2025). Facilitating topic modeling in tourism research: Comprehensive comparison of new AI technologies. Tourism Management, 106, 105007.
Kirilenko, A. P., Stepchenkova, S. O., & Dai, X. (2021). Automated topic modeling of tourist reviews: does the Anna Karenina principle apply?. Tourism Management, 83, 104241.
Minasi, S. M., Lohmann, G., & Valduga, V. (2023). Geographic Information Systems are critical tools to manage wine tourism regions. Tourism Geographies, 25(1), 198-219.
Morales-Pérez, S., Garay, L., & Wilson, J. (2022). Airbnb’s contribution to socio-spatial inequalities and geographies of resistance in Barcelona. Tourism Geographies, 24(6-7), 978-1001.
Nhan, M., Kralj, A., Moyle, B., & Liu, B. (2025). Physiological measurement techniques in virtual tourism research: three caveats for future studies. Tourism Recreation Research, 1-9.
Stergiou, D. P., & Nella, A. (2024). ChatGPT and Tourist Decision‐Making: An Accessibility–Diagnosticity Theory Perspective. International Journal of Tourism Research, 26(5), e2757.
Su, L., Stepchenkova, S., & Kirilenko, A. P. (2019). Online public response to a service failure incident: Implications for crisis communications. Tourism Management, 73, 1-12.
Zhang, H., Huang, R., Zhang, Y., & Buhalis, D. (2022). Cultural ecosystem services evaluation using geolocated social media data: A review. Tourism Geographies, 24(4-5), 646-668.
