IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2105.07727.html
   My bibliography  Save this paper

Using social network and semantic analysis to analyze online travel forums and forecast tourism demand

Author

Listed:
  • A Fronzetti Colladon
  • B Guardabascio
  • R Innarella

Abstract

Forecasting tourism demand has important implications for both policy makers and companies operating in the tourism industry. In this research, we applied methods and tools of social network and semantic analysis to study user-generated content retrieved from online communities which interacted on the TripAdvisor travel forum. We analyzed the forums of 7 major European capital cities, over a period of 10 years, collecting more than 2,660,000 posts, written by about 147,000 users. We present a new methodology of analysis of tourism-related big data and a set of variables which could be integrated into traditional forecasting models. We implemented Factor Augmented Autoregressive and Bridge models with social network and semantic variables which often led to a better forecasting performance than univariate models and models based on Google Trend data. Forum language complexity and the centralization of the communication network, i.e. the presence of eminent contributors, were the variables that contributed more to the forecasting of international airport arrivals.

Suggested Citation

  • A Fronzetti Colladon & B Guardabascio & R Innarella, 2021. "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers 2105.07727, arXiv.org.
  • Handle: RePEc:arx:papers:2105.07727
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2105.07727
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gunter, Ulrich & Önder, Irem, 2015. "Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data," Tourism Management, Elsevier, vol. 46(C), pages 123-135.
    2. Mahalia Jackman & Simon Naitram, 2015. "Research Note: Nowcasting Tourist Arrivals in Barbados – Just Google it!," Tourism Economics, , vol. 21(6), pages 1309-1313, December.
    3. Amy Van Looy, 2016. "Social Media Management," Springer Texts in Business and Economics, Springer, number 978-3-319-21990-5, June.
    4. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    5. Minhyung Kang & Byoungsoo Kim & Peter Gloor & Gee‐Woo Bock, 2011. "Understanding the effect of social networks on user behaviors in community‐driven knowledge services," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(6), pages 1066-1074, June.
    6. Hirashima, Ashley & Jones, James & Bonham, Carl S. & Fuleky, Peter, 2017. "Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism," Annals of Tourism Research, Elsevier, vol. 63(C), pages 191-202.
    7. Yared H. Kidane & Peter A. Gloor, 2007. "Correlating temporal communication patterns of the Eclipse open source community with performance and creativity," Computational and Mathematical Organization Theory, Springer, vol. 13(1), pages 17-27, March.
    8. George Athanasopoulos & Ashton de Silva, 2010. "Multivariate exponential smoothing for forecasting tourist arrivals to Australia and New Zealand," Monash Econometrics and Business Statistics Working Papers 11/09, Monash University, Department of Econometrics and Business Statistics.
    9. Li, Yunpeng & Hu, Clark & Huang, Chao & Duan, Liqiong, 2017. "The concept of smart tourism in the context of tourism information services," Tourism Management, Elsevier, vol. 58(C), pages 293-300.
    10. Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    11. Juan L. Eugenio-Martin, 2016. "Estimating the Tourism Demand Impact of Public Infrastructure Investment: The Case of Malaga Airport Expansion," Tourism Economics, , vol. 22(2), pages 254-268, April.
    12. Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2012. "Testing for Granger non-causality in heterogeneous panels," Economic Modelling, Elsevier, vol. 29(4), pages 1450-1460.
    13. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
    14. Hudson, Simon & Roth, Martin S. & Madden, Thomas J. & Hudson, Rupert, 2015. "The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees," Tourism Management, Elsevier, vol. 47(C), pages 68-76.
    15. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    16. Concha Artola & Fernando Pinto & Pablo de Pedraza García, 2015. "Can internet searches forecast tourism inflows?," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 103-116, April.
    17. Hwang, Yeong-Hyeon & Jani, Dev & Jeong, Ho Kyun, 2013. "Analyzing international tourists' functional information needs: A comparative analysis of inquiries in an on-line travel forum," Journal of Business Research, Elsevier, vol. 66(6), pages 700-705.
    18. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    19. Sparks, Beverley A. & Browning, Victoria, 2011. "The impact of online reviews on hotel booking intentions and perception of trust," Tourism Management, Elsevier, vol. 32(6), pages 1310-1323.
    20. Amaro, Suzanne & Duarte, Paulo & Henriques, Carla, 2016. "Travelers’ use of social media: A clustering approach," Annals of Tourism Research, Elsevier, vol. 59(C), pages 1-15.
    21. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    22. Xue, Lan & Kerstetter, Deborah & Hunt, Carter, 2017. "Tourism development and changing rural identity in China," Annals of Tourism Research, Elsevier, vol. 66(C), pages 170-182.
    23. Llodrà-Riera, Isabel & Martínez-Ruiz, María Pilar & Jiménez-Zarco, Ana Isabel & Izquierdo-Yusta, Alicia, 2015. "A multidimensional analysis of the information sources construct and its relevance for destination image formation," Tourism Management, Elsevier, vol. 48(C), pages 319-328.
    24. Gloor, Peter & Fronzetti Colladon, Andrea & Giacomelli, Gianni & Saran, Tejasvita & Grippa, Francesca, 2017. "The impact of virtual mirroring on customer satisfaction," Journal of Business Research, Elsevier, vol. 75(C), pages 67-76.
    25. Li, Gang & Law, Rob & Vu, Huy Quan & Rong, Jia & Zhao, Xinyuan (Roy), 2015. "Identifying emerging hotel preferences using Emerging Pattern Mining technique," Tourism Management, Elsevier, vol. 46(C), pages 311-321.
    26. Bieger, Thomas & Wittmer, Andreas, 2006. "Air transport and tourism—Perspectives and challenges for destinations, airlines and governments," Journal of Air Transport Management, Elsevier, vol. 12(1), pages 40-46.
    27. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    28. Minhyung Kang & Byoungsoo Kim & Peter Gloor & Gee-Woo Bock, 2011. "Understanding the effect of social networks on user behaviors in community-driven knowledge services," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(6), pages 1066-1074, June.
    29. Xiang, Zheng & Magnini, Vincent P. & Fesenmaier, Daniel R., 2015. "Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet," Journal of Retailing and Consumer Services, Elsevier, vol. 22(C), pages 244-249.
    30. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
    31. Cao, Zheng & Li, Gang & Song, Haiyan, 2017. "Modelling the interdependence of tourism demand: The global vector autoregressive approach," Annals of Tourism Research, Elsevier, vol. 67(C), pages 1-13.
    32. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Stylos, Nikolaos, 2017. "‘You will like it!’ using open data to predict tourists' response to a tourist attraction," Tourism Management, Elsevier, vol. 60(C), pages 430-438.
    33. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    34. Alain Dupeyras & Neil MacCallum, 2013. "Indicators for Measuring Competitiveness in Tourism: A Guidance Document," OECD Tourism Papers 2013/2, OECD Publishing.
    35. Ho, Jason Y.C. & Dempsey, Melanie, 2010. "Viral marketing: Motivations to forward online content," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1000-1006, September.
    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. Katerina Volchek & Anyu Liu & Haiyan Song & Dimitrios Buhalis, 2019. "Forecasting tourist arrivals at attractions: Search engine empowered methodologies," Tourism Economics, , vol. 25(3), pages 425-447, May.
    2. Shaolong Suna & Dan Bi & Ju-e Guo & Shouyang Wang, 2020. "Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach," Papers 2002.08021, arXiv.org, revised Mar 2020.
    3. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    4. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.
    5. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    6. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.
    7. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    8. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    9. Silvia Emili & Paolo Figini & Andrea Guizzardi, 2020. "Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data," Tourism Economics, , vol. 26(7), pages 1129-1151, November.
    10. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    11. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    12. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    13. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    14. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
    15. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    16. Javier Sebastian, 2016. "Blockchain in financial services: Regulatory landscape and future challenges," Working Papers 16/21, BBVA Bank, Economic Research Department.
    17. F. Antolini & L. Grassini, 2019. "Foreign arrivals nowcasting in Italy with Google Trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2385-2401, September.
    18. Liu, Yuan-Yuan & Tseng, Fang-Mei & Tseng, Yi-Heng, 2018. "Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 123-134.
    19. Haodong Sun & Yang Yang & Yanyan Chen & Xiaoming Liu & Jiachen Wang, 2023. "Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model," Information Technology & Tourism, Springer, vol. 25(2), pages 205-233, June.
    20. Yang, Yang & Fan, Yawen & Jiang, Lan & Liu, Xiaohui, 2022. "Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?," Annals of Tourism Research, Elsevier, vol. 93(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2105.07727. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.