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Semantic social media analysis of Chinese tourists in Switzerland

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Abstract

In recent years, Sina Weibo, a Twitter-like social network service in China, has attracted attention from scholars in the domain of information systems, as the spread and influence of users’ opinions are increasingly important, particularly in the tourism industry. This study examined the behaviors of Chinese tourists in Switzerland by adopting a semantic-based linked data methodology. A total of 103,778 Weibo messages shared with Swiss locations were collected between January 2013 and April 2015. We addressed questions about Chinese travelers’ profiles, trends in keywords, and differences between first time and repeat visitors. Moreover, we implemented a semantic search engine by employing linked data technologies to provide useful information about Chinese tourists in Switzerland, both for the tourism industry and individual tourists.

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References

  • Auer S, Bizer C, Kobilarov G et al (2007) DBpedia: A nucleus for a web of open data. Proceedings of the 6th International The Semantic Web and 2Nd Asian Conference on Asian Semantic Web Conference. Springer-Verlag, Berlin, pp 722–735

    Google Scholar 

  • Bai S, Hao B, Li A et al (2013) Predicting big five personality traits of microblog users. In: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), pp 501–508

  • Bontcheva K, Cunningham H (2011) Semantic annotations and retrieval: manual, semiautomatic, and automatic generation. Handbook of semantic web technologies. Springer, Berlin Heidelberg, pp 77–116

    Chapter  Google Scholar 

  • Bontcheva K, Rout D (2014) Making sense of social media streams through semantics: a survey. Semantic Web 5:373–403

    Google Scholar 

  • Burgess S, Sellitto C, Cox C, Buultjens J (2009) User-generated content (UGC) in tourism: benefits and concerns of online consumers. In: Information systems in a globalising world: challenges, ethics and practices: Proceedings of the 17th European Conference on Information Systems. Verona, Italy

  • Cheng M, Edwards D (2015) Social media in tourism: a visual analytic approach. Curr Issues Tour 18:1080–1087. doi:10.1080/13683500.2015.1036009

    Article  Google Scholar 

  • China Internet Watch (2015) Sina Weibo revenue and statistics. http://www.businessofapps.com/sina-weibo-revenue-and-statistics/. Accessed 09 March 2016

  • Chua T-S, Luan H, Sun M, Yang S (2012) NExT: NUS-Tsinghua Center for extreme search of user-generated content. IEEE Multimed 19:81–87

    Article  Google Scholar 

  • Cox C, Burgess S, Sellitto C, Buultjens J (2009) The role of user-generated content in tourists’ travel planning behavior. J Hosp Mark Manag 18:743–764. doi:10.1080/19368620903235753

    Google Scholar 

  • Djuana E, Xu Y, Li Y (2011) Constructing tag ontology from folksonomy based on WordNet. In: Kommers P, Zhang J, Issa T, Isaías P (eds) Proceedings of the IADIS International Conference on Internet Technologies and Society 2011. International Association for Development of the Information Society (IADIS), The East China Normal University, Shanghai

    Google Scholar 

  • Doyd D, Crawford K (2012) Critical questions for big data. Inf Commun Soc 15:662–679. doi:10.1080/1369118X.2012.678878

    Article  Google Scholar 

  • Forrester (2012) Forrsights business decision-makers survey Q4 2012. https://www.forrester.com/Forrsights+Business+DecisionMakers+Survey+Q4+2012/-/E-SUS1851. Accessed 09 March 2016

  • Francalanci C, Hussain A (2016) Discovering social influencers with network visualization: evidence from the tourism domain. Inf Technol Tour 16:103–125. doi:10.1007/s40558-015-0030-3

    Article  Google Scholar 

  • Fuchs M, Höpken W, Lexhagen M (2014) Big data analytics for knowledge generation in tourism destinations–a case from Sweden. J Destin Market Manag 3:198–209. doi:10.1016/j.jdmm.2014.08.002

    Google Scholar 

  • Gabrilovich E, Markovitch S (2007) Computing semantic relatedness using wikipedia-based explicit semantic analysis. Proceedings of the 20th International Joint Conference on Artifical Intelligence. Morgan Kaufmann Publishers Inc., San Francisco, pp 1606–1611

    Google Scholar 

  • Garcia-Crespo A, Lopez-Cuadrado JL, Colomo-Palacios R, Gonzalez-Carrasco I, Ruiz-Mezcua B (2011) Sem-Fit: a semantic based expert system to provide recommendations in the tourism domain. Expert Syst Appl 38:13310–13319. doi:10.1016/j.eswa.2011.04.152

    Article  Google Scholar 

  • Guan W, Gao H, Yang M et al (2014) Analyzing user behavior of the micro-blogging website Sina Weibo during hot social events. Phys Stat Mech Its Appl 395:340–351. doi:10.1016/j.physa.2013.09.059

    Article  Google Scholar 

  • Hepp M (2010) HyperTwitter: collaborative knowledge engineering via twitter messages. International Conference on Knowledge Engineering and Knowledge Management. Springer, Berlin Heidelberg, pp 451–461

    Google Scholar 

  • Hu T, Kalbaska N, Cantoni L (2013) A Picturesque Online Representation of Switzerland: a research conducted on Chinese social network Weibo. http://www.sinoptic.ch/textes/recherche/2013/201304_USI_swiss-images-cn.pdf. Accessed 09 March 2016

  • Hwang HS, Choi EK (2016) Exploring gender differences in motivations for using Sina Weibo. KSII Trans Internet Inf Syst 10:1429–1441

    Google Scholar 

  • Jacobsen JKS, Munar AM (2012) Tourist information search and destination choice in a digital age. Tour Manag Perspect 1:39–47. doi:10.1016/j.tmp.2011.12.005

    Article  Google Scholar 

  • Jansen BJ, Zhang M, Sobel K, Chowdury A (2009) Twitter power: tweets as electronic word of mouth. J Am Soc Inf Sci Technol 60:2169–2188. doi:10.1002/asi.21149

    Article  Google Scholar 

  • Kiryakov A, Popov B, Terziev I, Manov D, Ognyanoff D (2004) Semantic annotation, indexing, and retrieval. Web Semant Sci Serv Agents World Wide Web 2:49–79

    Article  Google Scholar 

  • Kleinen A, Scherp A, Staab S (2014) Interactive faceted search and exploration of open social media data on a touchscreen mobile phone. Multimed Tools Appl 71:39–60. doi:10.1007/s11042-013-1366-3

    Article  Google Scholar 

  • Kobilaro G, Scott T, Raimond Y, Oliver S, Sizemore C, Smethurst M, Bizer C, Lee R (2009) Media meets semantic web—how the bbc uses dbpedia and linked data to make connections. The semantic web: research and applications. Springer, Berlin Heidelberg, pp 723–737

    Chapter  Google Scholar 

  • Lazer D, Pentland A, Adamic L et al (2009) Social science. Computational social science. Science 323:721–723. doi:10.1126/science.1167742

    Article  Google Scholar 

  • Li Y, Bontcheva K (2007). Hierarchical, perceptron-like learning for ontology-based information extraction. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 777–786

  • Li Y, Gao H, Yang M et al (2015) What are Chinese talking about in hot weibos? Phys Stat Mech Its Appl 419:546–557. doi:10.1016/j.physa.2014.10.043

    Article  Google Scholar 

  • Litvin SW, Goldsmith RE, Pan B (2008) Electronic word-of-mouth in hospitality and tourism management. Tour Manag 29:458–468. doi:10.1016/j.tourman.2007.05.011

    Article  Google Scholar 

  • MakeAWebSiteHub (2015) 2015 Chinese social media statistics and trends Infographic. http://makeawebsitehub.com/chinese-social-media-statistics/. Accessed 09 March 2016

  • Manyika J, Chui M, Brown B et al (2011) Big data: the next frontier for innovation, competition, and productivity | McKinsey & Company. http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation. Accessed 8 Mar 2016

  • Marr B (2015) Why only one of the 5 Vs of big data really matters. http://www.ibmbigdatahub.com/blog/why-only-one-5-vs-big-data-really-matters. Accessed 01 June 2016

  • Miguéns J, Baggio R, Costa C (2008) Social media and tourism destinations: TripAdvisor case study. Adv Tour Res 26:26–28. doi:10.6092/1970-9870/2501

    Google Scholar 

  • Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38:39–41. doi:10.1145/219717.219748

    Article  Google Scholar 

  • Moro A, Cecconi F, Navigli R (2014a) Multilingual word sense disambiguation and entity linking for everybody. Proceedings of the 2014 International Conference on Posters and Demonstrations Track, vol 1272. CEUR-WS.org, Aachen, pp 25–28

    Google Scholar 

  • Moro A, Raganato A, Navigli R (2014b) Entity linking meets word sense disambiguation: a unified approach. Trans Assoc Comput Linguist 2:231–244

    Google Scholar 

  • Muscanell NL, Guadagno RE (2012) Make new friends or keep the old: gender and personality differences in social networking use. Comput Hum Behav 28:107–112

    Article  Google Scholar 

  • Navigli R, Ponzetto SP (2010) BabelNet: building a very large multilingual semantic network. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Stroudsburg, pp 216–225

    Google Scholar 

  • Passant A, Laublet P (2008) Meaning of a tag: a collaborative approach to bridge the gap between tagging and linked data. In: Proceedings of the Linked Data on the Web Workshop (LDOW), Beijing, China

  • Pérez J, Arenas M, Gutierrez C (2009) Semantics and complexity of SPARQL. ACM Trans Database Syst 34:16:1–16:45. doi:10.1145/1567274.1567278

  • Pirolli P (2009) Powers of 10: modeling complex information seeking systems at multiple scales. IEEE Comput 42:33–40

    Article  Google Scholar 

  • Pöschko J (2011) Exploring twitter hashtags. arXiv preprint arXiv:1111.6553

  • Shen H, Songm C, Li M, Jiang Q (2015) Shaping destination images through social networking sites: a case study of Singapore. Adv Hosp Lesure 11:89–107

    Article  Google Scholar 

  • Simms A (2012) Online user-generated content for travel planning—different for different kinds of trips? E-Rev Tour Res 10:76–85

    Google Scholar 

  • Switzerland Tourism (2013) Research Report China: Market Analysis and Insight. http://www.stnet.ch/files/?id=63853. Accessed 01 Nov 2014

  • Vicient C, Moreno A (2015) Unsupervised topic discovery in micro-blogging networks. Expert Syst Appl 42:6472–6485. doi:10.1016/j.eswa.2015.04.014

    Article  Google Scholar 

  • Wick M, Vatant B (2012) The geonames geographical database. http://geonames.org. Accessed 09 March 2016

  • Xiang Z, Gretzel U (2010) Role of social media in online travel information search. Tour Manag 31:179–188. doi:10.1016/j.tourman.2009.02.016

    Article  Google Scholar 

  • Xiang Z, Schwartz Z, Gerdes JH, Uysal M (2015) What can big data and text analytics tell us about hotel guest experience and satisfaction? Int J Hosp Manag 44:120–130. doi:10.1016/j.ijhm.2014.10.013

    Article  Google Scholar 

  • Yoo KH, Gretzel U (2010) Antecedents and impacts of trust in travel-related consumer-generated media. Inf Technol Tour 12:139–152. doi:10.3727/109830510X12887971002701

    Article  Google Scholar 

  • Yoo KH, Lee Y, Gretzel U, Fesenmaier DR (2009) Trust in travel-related consumer generated media. In: Höpken DW, Gretzel DU, Law DR (eds) Information and communication technologies in tourism 2009. Springer, Vienna, pp 49–59

  • Yu L, Asur S, Huberman BA (2011) What trends in Chinese social media. arXiv preprint arXiv:1107.3522

  • Zhang L, Pentina I (2012) Motivations and usage patterns of Weibo. Cyberpsychol Behav Soc Netw 15:312–317. doi:10.1089/cyber.2011.0615

    Article  Google Scholar 

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Acknowledgments

The work described in this paper was part of project SWICICO and was supported by the University of Applied Sciences and Arts Western Switzerland (HES-SO) under Grant Number 43080. We thank the contributors from HES-SO Valais-Wallis, Professor Anne Le Calvé and Fabian Cretton, for their help and work on the technologies of the Semantic Web and linked data. In addition, we thank Professor Dominique Genoud, Professor Maria Sokhn, and Jérôme Treboux, for their suggestions on big data mining. We also thank Professor Nicolas Délétroz and Simon Bosshart (Switzerland Tourism) for sharing their experiences in the domain of tourism, especially about the market of Chinese tourists in Switzerland. Moreover, we thank Huan Zhang (Sina Data Center) for his help and support in collecting the data from Sina Weibo. And, we also thank Ontotext who supported us with a research license for OWLIM, as well as Roberto Navigli and colleagues (Sapienza University of Rome) to provide us the research license for BabelNet. Finally, we thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.

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Authors and Affiliations

  1. Institute of Information Systems, University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis), Sierre, Switzerland

    Zhan Liu & Nicole Glassey Balet

  2. Global Center for Digital Business Transformation, International Institute for Management Development (IMD), Lausanne, Switzerland

    Jialu Shan

  3. School of Management, Hangzhou Dianzi University, Hangzhou, China

    Gang Fang

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  1. Zhan Liu
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Correspondence to Zhan Liu.

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Liu, Z., Shan, J., Glassey Balet, N. et al. Semantic social media analysis of Chinese tourists in Switzerland. Inf Technol Tourism 17, 183–202 (2017). https://doi.org/10.1007/s40558-016-0066-z

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