Our Scientific Contributions

• Siamak Seyfi, Albert Nsom Kimbu, Tan Vo-Thanh & Mustafeed Zaman (2025). Tourism, consent, and resistance: A Gramscian lens. Annals of Tourism Research, 115, 104056- [Abstract]
  This study examines how hegemonic ideologies shape women's participation in tourism in a theocracy. Applying Gramsci's theory of hegemony and a critical poststructural feminist lens, it analyses how Iran's politico-religious structures influence women's roles, employment, and mobility in tourism. Qualitative findings reveal strategies of compliance, negotiation, and resistance expressed through entrepreneurship, networking, and workplace practices. Concepts of ‘war of position’ and ‘passive revolution’ explain how women create space for agency without direct confrontation, reshaping visibility and legitimacy incrementally. The study advances tourism scholarship by situating women's agency within hegemonic structures of theocratic governance and extending Gramscian theory to show how gendered consent and resistance operate in tourism, while calling for gender-transformative policies that address inequality and support women's situated agency.

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• Mamédio, Diorgenes, Pina e Cunha, Miguel, Rego, Arménio & Clegg, Stewart (2025). Cinematic Tourists: Forms of Authenticity in Consumers' Reception of the Moroccan Film Industry. In Film and Tourism : Case Studies on Tourist Behavior (pp. 39-53)- [Abstract]
  Cinematic realities creatively produce imagined scenic environments that constitute seemingly `authentic' destinations that can attract tourists' attention. Not all is as it seems, however; hence we ask, ``How can paradoxes of authenticity fuel consumers' reception of products of the film industry?''. We use paradox theory to address this question. Virtual ethnography is used to capture tourists' perception of multifaceted contexts of authenticity in the Moroccan film industry. We categorize authenticity as multiform (real, fake and fake-authentic) through film case analysis. We reveal and discuss three paradoxical forms of authenticity: (i) authentic reality; (ii) merging fake reality; and (iii) genuinely authentically fake. Our findings highlight that, despite a fake reality, tourists interplay fakery with authenticity, transforming the imitation into something more original than the source.

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• Hugo Alatrista Salas, Gaël Chareyron, Sonia Djebali, Imen Ouled Dlala & Nicolas Travers (2025). From Flows to Graphs: Data-Driven Insights on Latent Overtourism with Frequent Pattern Mining. DOING workshop - ADBIS Conference, . Tampere, Finland- [Abstract]
  Overtourism presents complex and often hidden challenges for urban environ- ments, impacting residents, infrastructure, and visitor satisfaction. This study proposes a novel, data-driven methodology to detect and analyze latent over- tourism?the early, subtle warning signs of excessive tourism?before visible breakdowns occur. By leveraging user-generated content from Tripadvisor, a tem- poral circulation multidigraphs is modeled to capture tourist mobility. Using frequent subgraph mining algorithms, the approach identifies recurring tourist movement patterns across different urban scales. These patterns are then ana- lyzed in both spatial and temporal dimensions to detect hotspots and evaluate dynamic attractiveness through a Huff-based probabilistic model. The approach is applied to three cities of varying sizes revealing consistent tourist flows and areas under increasing pressure, suggesting early overtourism.

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• Guillaume Guérard, Quentin Gabot & Sonia Djebali (2025). Tourism profile measure for data?driven tourism segmentation. International Journal Of Machine Learning And Cybernetics, 16, 7107-7132- [Abstract]
  The digital revolution has brought about profound changes in research within the tourism segmentation field. The ease of grasping tourists' behaviors is facilitated by the digital traces left on social networks. Existing studies focusing on tourists' digital traces typically apply clustering algorithms to the tourism context. This paper introduces a novel measure, named tourism profile measure for determining tourism segmentation, also known as tourism profiling. The approach involves establishing a new clustering algorithm that centers on stays conducted by tourists, utilizing both the context and content of the trips. The proposed measure is then simulated and experimentally evaluated using a real dataset across various periods and diverse nationalities, particularly in the context of the French capital, Paris.

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• Catherine Lejealle, Insaf Khelladi & Saeedeh Rezaee Vessal (2025). Exploring tourist experience in mixed reality: An integrated framework approach. Advances in Tourism Marketing Conference, . Heraklion, Crete, Greece

• Scuotto, Veronica, Karagöz, Deniz, Farronato, Nicola & Alon, Ilan (2024). A destination’s personality as a factor in tourists’ environmental knowledge management. Journal of Knowledge Management, 28, 1185-1203- [Abstract]
  Environmental knowledge management (EKM) has been studied mainly owing to the increasing awareness of environmental issues. Such issues have generated a warning in the tourism industry that has stimulated a new wave of research on EKM. EKM forges landscape characteristics and so destination image. In turn, EKM sounds affecting tourism destination which calls for destination personality which shows a research context less explored. From a knowledge management perspective, The present research aims to investigate on EKM to understand how it leverages tourists' and destination personality.With the intent of exploring EKM, the research uses a quantitative analysis on a sample of 2,222 young Chinese tourists. In this context, EKM is linked with destination’s personality and tourists’ personalities, their satisfaction with the destination and their behavioral intentions.By SPSS regression model, EKM and destination personality are positively linked. This positive relationship is also reflected on destination personality and destination satisfaction, behavioral intention.The authors’ original contribution to the knowledge management literature extends the new wave of research on EKM. The research also proves the need to make a close collaboration between tourists, the local community and marketers. Marketers need to pay more attention to what tourists want to do and see in the place visited. In a nutshell, there is the need of enforcing and promoting EKM.

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• Mihaela Axente, Didier Decoupigny, Jeanne Kostrz, Charles Minart, Gaël Chareyron, Sébastien Jacquot, Mélanie Mondo & Nicolas Travers (2024). Les pratiques touristiques dans la destination Lille métropole : Analyse à partir des traces numériques des visiteurs. MEL

• Hugo Alatrista Salas, Gaël Chareyron, Sonia Djebali, Imen Ouled Dlala & Nicolas Travers (2024). SuPreME: Sequential Pattern Mining for Understanding Multiscale Seasonal Tourist Behavior. GdR MADICS, . Blois, France

• Hajer Kefi, Ekaterina Besson & Karina Sokolova (2024). Lifelong Learning through a Virtual Learning Environment: The Determinants of Success in the Tourism and Hospitality Sector. In Association of Information Systems (AIS) (Ed.), AMCIS 2024 Proceedings. Atlanta, USA: AIS Electronic Library (AISeL)- [Abstract]
  An extensive literature has explored and analyzed the predictors of e-learning success in the context of higher education. However, research is scarce on investigating direct and mediating roles and key aspects related to lifelong learning deployed within Virtual Learning Environments at the workplace. Building on a constructivist assumption of learning and on Transactional Distance Theory, a research model is developed to assess the determining effects of instructor's support, e-learners online interactions, learner self-motivation and perceived stress on e-learner satisfaction. 318 responses were collected from e-learners in the context of LLL in the tourism and hospitality sectors in France and subjected to structural equation modeling. Main results show the positive determining effects of instructor's support, e-learners online interactions, learner self-motivation on e-learner satisfaction. Whereas, perceived stress impacts directly and negatively e-learner satisfaction and moderates the effect of self-motivation on e-learner satisfaction. Perceived stress is nurtured and not reduced by e-learners interactions.

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• Sonia Djebali, Quentin Gabot & Guillaume Guérard (2023). Hierarchical Clustering and Measure for Tourism Profiling. In 6th APWeb-WAIM International Joint Conference on Web and Big Data (pp. 158-165). Nanjing, China:

• Mohamed Zaifri, Hamza Khalloufi, Fatima Zahra Kaghat, Ahmed Azough & Khalid Alaoui Zidani (2023). From Earlier Exploration to Advanced Applications: Bibliometric and Systematic Review of Augmented Reality in the Tourism Industry (2002-2022). Multimodal Technologies and Interaction, 7, 64- [Abstract]
  Augmented reality has emerged as a transformative technology, with the potential to revolutionize the tourism industry. Nonetheless, there is a scarcity of studies tracing the progression of AR and its application in tourism, from early exploration to recent advancements. This study aims to provide a comprehensive overview of the evolution, contexts, and design elements of AR in tourism over the period (2002-2022), offering insights for further progress in this domain. Employing a dual-method approach, a bibliometric analysis was conducted on 861 articles collected from the Scopus and Web of Science databases, to investigate the evolution of AR research over time and across countries, and to identify the main contexts of the utilization of AR in tourism. In the second part of our study, a systematic content analysis was conducted, focusing on a subset of 57 selected studies that specifically employed AR systems in various tourism situations. Through this analysis, the most commonly utilized AR design components, such as tracking systems, AR devices, tourism settings, and virtual content were summarized. Furthermore, we explored how these components were integrated to enhance the overall tourism experience. The findings reveal a growing trend in research production, led by Europe and Asia. Key contexts of AR applications in tourism encompass cultural heritage, mobile AR, and smart tourism, with emerging topics such as artificial intelligence (AI), big data, and COVID-19. Frequently used AR design components comprise mobile devices, marker-less tracking systems, outdoor environments, and visual overlays. Future research could involve optimizing AR experiences for users with disabilities, supporting multicultural experiences, integrating AI with big data, fostering sustainability, and remote virtual tourism. This study contributes to the ongoing discourse on the role of AR in shaping the future of tourism in the post COVID-19 era, by providing valuable insights for researchers, practitioners, and policymakers in the tourism industry.

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• Mohamed Zaifri, Hamza Khalloufi, Fatima Zahra Kaghat, Abdessamad Benlahbib, Ahmed Azough & Khalid Alaoui Zidani (2023). Enhancing Tourist Experiences in Crowded Destinations through Mobile Augmented Reality: A Comparative Field Study. International Journal of Interactive Mobile Technologies, 17, 92-113- [Abstract]
  Mobile augmented reality (MAR) has gained significant attention in the tourism sector as a way to enhance the visitor experience. The rapid advancements in mobile computing and sensor technologies have facilitated the widespread use of geospatial augmented reality (AR) applications by tourists when exploring popular destinations. To analyze the impact of AR technology on the tourism experience, we developed the FEZAR mobile application. This application serves as the focal point of our study, allowing us to evaluate user performance using a comparative experimental approach. To ensure the usability of the FEZAR application, professionals with expertise in mobile technologies, including AR, performed rigorous testing and evaluation of the application. Through their evaluations, significant usability issues were identified and resolved, resulting in the application being well-received by the experts. Subsequently, a comparative field study was conducted in Fez's old medina, a crowded UNESCO heritage site, involving users (N = 40) who were randomly assigned to experimental and control groups in equal distribution. The results of the study revealed that the proposed AR model had a significant positive impact on user visits. Compared to other forms of media, AR offers more informative and enjoyable experiences. Additionally, it effectively helps locate monuments in crowded tourism settings. The findings of this research make a valuable contribution to the ongoing discussion regarding the impact of evaluating MAR user interfaces on increasing visitor engagement with tourist destinations.

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• Gaël Chareyron (2023). « EN MODE AVION ». Greenpeace France technical report.

• Sonia Djebali, Nicolas Travers & Gaël Chareyron (2022). Indicateurs de Mesure pour la Mobilité Touristique. 40ème Congrès INFORSID 2022, . Dijon, France- [Abstract]
  Les traces numériques laissées par les utilisateurs sur les réseaux sociaux sont deve- nues un moyen populaire d'analyse du comportement des touristes dont l'analyse a un impact dans le marketing touristique pour construire des outils d'aide à la décision dans le tourisme. Nous proposons des mesures pour capturer la mobilité et la propagation touristique sur diffé- rentes zones qui s'appuie sur le réseau TripAdvisor et la base de données graphes Neo4j.

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• Quentin Gabot, Guillaume Guérard & Sonia Djebali (2022). Tourists profiling by interest analysis. Groupe de Recherche : Masses de Données, Informations et Connaissances en Sciences (MADICS), CNRS. Lyon, France- [Abstract]
  With the recent digital revolution, analyzing of tourists' behaviorsand research fields associated with it have changed profoundly.It is now easier to examine behaviors of tourists using digital traces they leave during their travels. The studies conducted on diverse aspects of tourism focus on quantitative aspects of digital traces to reach its conclusions.In this paper, we suggest a study focused on both qualitative andquantitative aspect of digital traces to understand the dynamics governing tourist behavior, especially those concerning attractions networks.

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• Hugo Prevoteau, Sonia Djebali, Laiping Zhao & Nicolas Travers (2022). Propagation measure on circulation graphs for tourism behavior analysis. In SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, April 25 - 29, 2022 (pp. 556-563)

• Guillaume Guérard, Sonia Djebali & Quentin Gabot (2022). Tourists Profiling by Interest Analysis. In International Conference on Advanced Data Mining and Applications (pp. 42-53). Virtual: Springer, Cham- [Abstract]
  With the recent digital revolution, analyzing of tourists' behaviors and research elds associated with it have changed profoundly. It is now easier to examine behaviors of tourists using digital traces they leave during their travels. The studies conducted on diverse aspects of tourism focus on quantitative aspects of digital traces to reach its conclusions. In this paper, we suggest a study focused on both qualitative and quantitative aspect of digital traces to understand the dynamics governing tourist behavior, especially those concerning attractions networks.

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• Gaël Chareyron & Sébastien Jacquot (2022). Studying tourism practices at heritage sites through online published content: the case of the World Heritage Site Strasbourg, Grande-Ile and Neustadt. In Silvia De Ascaniis & Lorenzo Cantoni (Eds.), Handbook on Heritage, Sustainable Tourism and Digital Media (pp. 153-166). Edward Elgar Publishing- [Abstract]
  The development of web 2.0 has transformed the role of tourists in tourism promotion, in relation with the development of user-generated content. This chapter is a methodological contribution to the study of user-generated content on tourist social networks, which represent the point of view of the tourists themselves. It presents methods to study tourism practices at heritage sites by analyzing images from Flickr and reviews published online on TripAdvisor. This analysis of online social networks is a fundamental tool now for the tourist observation of heritage destinations, to understand how the tourists experience and live the destination, and the meanings they give to the visited areas and sites. This requires combining cartographic analysis, statistical analysis and content analysis. We apply these methods to study tourism in Strasbourg, in relation with the extension of the UNESCO perimeter in 2018. The analysis of the geolocalization of Flickr photographs and the content analysis of TripAdvisor reviews enable us question the visibility and tourist coherence of the new perimeter, and to identify the representations attached to its monuments and sites.

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• Flavien Deseure-Charron, Sonia Djebali & Guillaume Guérard (2022). Clustering Method for Touristic Photographic Spots Recommendation. In 18th International Conference on Advanced Data Mining and Applications. Brisbane, Australia:

• Elena Chatzopoulou & Nicolas Travers (2022). Authenticity Goes Digital: A Big Data Analysis of the Influence of the Country of Origin and Authenticity Perceptions on TripAdvisor Ethnic Restaurant Reviews. In Academy of Marketing Science Annual Conference (pp. 3-15). online: Springer- [Abstract]
  Authenticity perceptions are subjectively driven and rely on social constructions making the concept hard to be defined. The current study is following a big data approach to capture perceptions and beliefs concerning the authenticity of ethnic restaurants and also when online positive reviews are given about authenticity under the influence of a visit to the country of origin. The key idea of our method relies on the analysis of a 3-step characterization of a big data repository extracted from TripAdvisor. Step 0 concerns reviews made for Italian restaurants before consumers visit Italy, step 1 concerns the reviews made while consumers were in Italy and step 2 concerns reviews made after they visited Italy. This characterization exploits both sentiment analysis and graph data models. Our findings propose a depiction of authenticity for ethnic restaurants via e-word of mouth. With a big data analysis on TripAdvisor, we provided an analysis on both ratings and comments which showed the impact of authenticity. As such, consumers, after visiting the country of origin, were more critical while they provided lower ratings and they were also focusing more on authentic atmosphere and service, showing evolution of their online reviews.

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• Guillaume Guérard & Sonia Djebali (2021). Tourism Management through the Big Data Paradigm. In David Chavalarias, Hocine Cherifi (Ed.), French Regional Conference on Complex Systems FRCCS. virtual: Springer - Applied Network Science Journal- [Abstract]
  This paper highlights the two-ways relationship between data and entities producing or generating these data in the context of tourism management. Indeed, tourists leave various traces of their planning and trip on social media and networks. Moreover, studying those data to feed tourism marketing also affects the future data left by tourists. Understanding the intrinsic and extrinsic relationship between entities should significantly improve the knowledge in this research field.

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• Guillaume Guérard, Sonia Djebali, Bi Chongke & Theo Demessance (2021). Hidden Markov Model to Predict Tourists Visited Places. In Vana Kalogeraki, Nick Koudas, Jianliang Xu (Ed.), 22nd IEEE International Conference on Mobile Data Management (pp. 209-216). virtual: IEEE- [Abstract]
  Nowadays, social networks is become a popular means of analyzing tourist behavior, thanks to the digital traces left by travelers during their stays on these networks. The massive amount of data generated by the propensity of tourists to share comments and photos during their trip makes it possible to model their journeys and analyze their behavior. Predicting the next movement of tourist plays a key role in tourism marketing to understand demand and improve decision support. In this paper, we propose a method to understand and to learn tourists' movements based on social network data analysis to predict the future movements. The method relies on a machine learning grammatical inference algorithm. A major contribution in this paper is to adapt the grammatical inference algorithm to the context of big data. Our method produces a hidden Markov model representing the movements of a group of tourists. The hidden Markov model is flexible and editable with new data. The capital city of France, Paris is selected to demonstrate the efficiency of the proposed methodology.

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• Gaël Chareyron, Saskia Cousin & Sébastien Jacquot (2021). L'Europe rythmée par ses visiteurs. 20 ans de commentaires géolocalisés et chronoréférencés. De Facto, 27, 46-51- [Abstract]
  Le tourisme est-il une migration privilégiée ? Cette animation invite à s'extraire de l'opposition Tourisme/Migration pour examiner les mobilités privilégiés du point de vue des traces laissées sur la plateforme numérique TripAdvisor À S'EXTRAIRE DE L'OPPOSITION TOURISME /MIGRATION POUR EXAMINER LES MOBILITÉS PRIVILÉGIÉES DU POINT DE VUE DES TRACES LAISSÉES SUR LA PLATEFORME NUMÉRIQUE TRIPADVISOR.

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• Gaël Chareyron, Saskia Cousin & Sébastien Jacquot (2021). Crise du tourisme et résistances des vacances. Valeurs et pratiques des mobilités de loisirs en période de pandémie. Mondes du Tourisme, 20- [Abstract]
  Selon l'Organisation mondiale du tourisme (OMT), la pandémie a réduit de 70% le tourisme international en 2020. Dans les médias, l'arrêt des mobilités internationales se traduit par un discours sur « la fin du tourisme » ou la « mort du voyage ». À l'été 2020, la plupart des territoires français connaissent pourtant une saison estivale aussi, voire plus fréquentée que les années précédentes. Comment expliquer ce décalage ? En s'appuyant sur l'analyse critique des indicateurs statistiques du tourisme, des observations de terrain et l'étude des commentaires postés sur les plateformes de réservation touristiques, cet article propose l'hypothèse suivante : la pandémie révélé la fragilité de l'industrie internationale du tourisme, mais aussi son emprise sur les normes, les valeurs et les imaginaires de l'ailleurs et du temps « autre ». Échappant à l'industrie du tourisme et à ses indicateurs, les pratiques de vacances populaires ont résisté plus qu'elles ne se sont radicalement transformées. Surtout, elles sont devenues visibles et valorisées. Dans ce contexte, la notion de valence différentielle nous permet d'appréhender l'actuelle inversion des valeurs associées respectivement au tourisme international et aux vacances populaires.

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• Sonia Djebali, Nicolas Loas & Nicolas Travers (2020). Indicators for Measuring Tourist Mobility. In Web Information Systems Engineering - WISE 2020 - 21st International Conference, Amsterdam, The Netherlands, October 20-24, 2020, Proceedings, Part I (pp. 398-413)

• Sonia Djebali & Guillaume Guérard (2020). Prédiction des comportements touristiques par minage des motifs et des règles séquentielles. In 21ème édition du congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision. Montpellier, France:

• H. Skinner, E. Chatzopoulou & M. Gorton (2020). Perceptions of localness and authenticity regarding restaurant choice in tourism settings. Journal Of Travel & Tourism Marketing, 37, 155-168

• Gaël Chareyron & Sébastien Jacquot (2020). Star Architecture from Below: Narratives and Images from Social Networks. In Nadia Alaily-Mattar, Davide Ponzini, Alain Thierstein (Ed.), About Star Architecture (pp. 133-150). Springer, Cham- [Abstract]
  This chapter studies star architecture through the analysis of tags and comments on social networks (TripAdvisor and Instagram). The objective is the analysis of star architectural projects from below, that is, the way visitors depict and frame star architecture and express aesthetical judgement, to characterise ?star architecture? and ?iconicity? from the point of view of its reception. The analysis of the comments on a selection of sites shows the importance of the architectural component in the tourist experience, not reducible to the mention of the star architect, but presenting multiple types of discourse, pluralising the reception of star architecture. The purely ?star? architectural dimension, based on the notoriety of the architect, can then be distinguished from the elements referring to the visual effects of buildings or to a perception of the building in its environment, closer to the notion of iconic building.

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• Lillia Ben Baccar, Sonia Djebali & Guillaume Guérard (2019). Tourist's Tour Prediction by Sequential Data Mining Approach. In Advanced Data Mining and Applications, 15th International Conference, ADMA 2019 (pp. pp. 681-695). Dalian, China: - [Abstract]
  This paper answers the problem of predicting future behaviour tourist based on past behaviour of an individual tourist. The individual behaviour is naturally an indicator of the behaviour of other tourists. The prediction of tourists movement has a crucial role in tourism marketing to create demand and assist tourists in decision-making. With advances in information and communication technology, social media platforms generate data from millions of people from different countries during their travel. The main objective of this paper is to consider sequential data-mining methods to predict tourist movement based on Instagram data. Rules emerge from those ones are exploited to predict future behaviors. The originality of this approach is a combination between pattern mining to reduce the size of data and the automata to condense the rules. The capital city of France, Paris is selected to demonstrate the utility of the proposed methodology.

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• Lillia Ben Baccar, Sonia Djebali & Guillaume Guérard (2019). Prédiction de Circuit Touristique par Data Mining Séquentielle. Premier Symposium GDR MaDICS, . Rennes, France

• Gaël Chareyron, Ugo Quelhas & Nicolas Travers (2019). Tourism Analysis on Graphs with Neo4Tourism. In Web Information Systems Engineering - WISE 2019 Workshop, Demo, and Tutorial, Hong Kong and Macau, China, January 19-22, 2020, Revised Selected Papers (pp. 37-44)

• Gaël Chareyron, Ugo Quelhas & Nicolas Travers (2019). Neo4Tourism - A framework for Graph Data Analysis on Tourism. 35ème Conférence sur la Gestion de Données, . Lyon, France

• Sébastien Jacquot & Gaël Chareyron (2018). Grande Guerre : comment la commémoration se réinvente sur TripAdvisor. The Conversation

• Sébastien Jacquot, Gaël Chareyron & Saskia Cousin (2018). Le tourisme de mémoire au prisme du big data : l'exemple de la Somme. Rencontres économiques de la Défense, . Paris, France

• Sébastien Jacquot, Gaël Chareyron & Saskia Cousin (2018). Le tourisme de mémoire au prisme du « big data ». Cartographier les circulations touristiques pour observer les pratiques mémorielles. Mondes du Tourisme, 14- [Abstract]
  Battlefield tourism and memory tourism are the subject of public policies to build memorial destinations. Based on the analysis and the mapping of the practices and flows of visitors, but also their national origins and the meaning that they confer to their experiences, this article questions the tourist consistency of these memorial territorial constructions. The originality of our investigation relies on our sources and methods: the collection and the analysis of the digital footprints from social networks aimed at sharing experiences and pictures (TripAdvisor and Flickr), in order to identify the communities and the networks constituted by the practices, through their differentiations and at various scales.

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• Gaël Chareyron & Sairi Pineros (2018). Imaginaires, pratiques et expériences touristiques dans la région Centre-Val de Loire (France): Analyse via Flickr et TripAdvisor. Congrès régional 2018 de l'Union géographique internationale IGU, . Québec, Canada

• Sébastien Jacquot, Saskia Cousin, Gaël Chareyron & Jeanne Richon (2017). Cartographie des locations saisonnières dans l'arrondissement, évolution du phénomène à Paris. Les locations saisonnières dans le 4e arrondissement : Une désertification invisible ?, . Paris, France

• Stéphane Loiseau, Sonia Djebali, Thomas Raimbault, Bérangère Branchet & Gaël Chareyron (2017). Characterization of daily tourism behaviors based on place sequence analysis from photo sharing websites. In 2017 IEEE International Conference on Big Data (pp. 2760 - 2765). Boston, USA:

• Saskia Cousin, Gaël Chareyron & Sébastien Jacquot (2017). Big Data and Tourism. In Linda L. Lowry (Ed.), The SAGE International Encyclopedia of Travel and Tourism (pp. 151-155). SAGE Publications, Inc

• Gaël Chareyron (2017). Études des traces numériques laissées par les internautes, cas de Airbnb en Val de Marne. Rencontres observation des CDT/ADT, . Paris, France

• Gaël Chareyron (2017). Histoires de prêts. Mémoires et enjeux des prêts dans les musées. Aspects géopolitiques et diplomatiques des prêts, colloque international organisé avec la participation de l'Institut d'histoire moderne et contemporaine (IHMC-CNRS-ENS), . Paris, France

• Gaël Chareyron & Sébastien Jacquot (2017). Airbnb ou la vie rêvée des autres. The Conversation

• Gaël Chareyron & Sébastien Jacquot (2017). Tourisme et Patrimoine mondial depuis les réseaux sociaux touristiques. 8e séminaire de la Chaire UNESCO « Culture, Tourisme, Développement », . Paris, France

• Gaël Chareyron (2016). Les traces numériques pour identifier les pratiques touristiques et patrimoniales en périphérie. Les périphéries métropolitaines au prisme des mobilités touristiques et du patrimoine, . Paris, France

• Gaël Chareyron (2016). Sémiotique, linguistique, infocom, anthropologie, récits de voyage, littérature, informatique... A la croisée des disciplines. Jean-Didier Urbain , 40 ans de voyages indisciplinés, . Paris, France

• Gaël Chareyron (2016). Visiter Paris, sa banlieue, ses environs, de 1820 à aujourd'hui. Visiter Paris, sa banlieue, ses environs, de 1820 à aujourd hui, . Paris, France

• Gaël Chareyron (2016). Le Louvre dans un monde global : connexions, circulations, projections. Colloque Louvre Monde. Un lieu, des territoires, . Paris, France

• B. Branchet (2016). Tourisme : technologies des nouveaux acteurs, utilisation du Big Data. , . Monaco, Monaco

• Gaël Chareyron & Sébastien Jacquot (2015). Knowledge and Tracking of the tourist practices through big data. Workshop Digital Technologies and Tourism, . Paris, France

• Gaël Chareyron & Saskia Cousin (2015). Enquête, quête et requête: Big Data touristique, objet hirsute. Rencontres collectives EspacesTemps.net, . Lille, France

• Gaël Chareyron, Saskia Cousin & Sébastien Jacquot (2015). Les nouveaux contours du Paris Touristique. In x (Ed.), Livre Catalogue de l'exposition Bons Baisers de Paris (pp. 152-153). Actes Sud

• Gaël Chareyron, Saskia Cousin & Sébastien Jacquot (2015). Big data & Tourism. Tourism Studies Working Group Colloqium Series - UC Berkeley, . Berkeley, USA

• Gaël Chareyron, Saskia Cousin & Sébastien Jacquot (2015). Circulations touristiques : la quête des traces numériques. Retour sur une drôle d'enquête/requête. séminaire tourisme recherches institutions pratiques, . Paris, France

• Gaël Chareyron, Bérangère Branchet & Sébastien Jacquot (2015). Atelier Traces numériques touristiques : un terrain numérique ?. Rencontres Internationales des Jeunes Chercheurs en Tourisme, . Paris, France

• Gaël Chareyron, Bérangère Branchet & Jérôme Da Rugna (2015). Foules voyageuses et traces numériques : Le big data au service du tourisme. l'Expansion

• Bérangère Branchet, Gaël Chareyron & Sébastien Jacquot (2015). A new area tourist ranking method. In Proceedings of the 2015 IEEE International Conference on Big Data (Big Data) (pp. 2930-2932). Santa Clara, USA:

• Bérangère Branchet, Gaël Chareyron, Saskia Cousin, Jérôme Da Rugna, Daniel Gabay & Sébastien Jacquot (2015). Sur les traces de Trip Advisor : pister les commentateurs pour qualifier les territoires et identifier un coefficient de touristification. Une aventure pluridisciplinaire. 5ème colloque international pluridisciplinaire de l'Association ASTRES, . Paris, France

• Gaël Chareyron, Bérangère Branchet, Jérôme Da Rugna & Sébastien Jacquot (2014). Patrolling TripAdvisor: Re-purposing large data-sets in Tourism research. EspacesTemps.net

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