La influencia en redes sociales online bimodales a través del caso de tripadvisor

Autores/as

  • Arnaldo Mario Litterio Universidad Nacional del Sur. Departamento de Ciencias de la Administración. Bahía Blanca; Buenos Aires; Argentina.
  • Esteban Alberto Nantes Universidad Nacional del Sur. Instituto de Investigaciones Económicas y Sociales del Sur (IIESS) y Departamento de Economía. Bahía Blanca; Buenos Aires; Argentina.
  • Juan Manuel Larrosa Universidad Nacional del Sur. Departamento de Economía. Bahía Blanca; Buenos Aires; Argentina.

DOI:

https://doi.org/10.35305/iiata.v7i7.89

Palabras clave:

Análisis de Redes Sociales, Marketing Digital, Influenciadores, Web Scraping, Tripadvisor

Resumen

El presente trabajo propone una aplicación de herramientas provenientes del análisis de redes sociales y de programación para la explotación de información de una comunidad online, más específicamente una red social bimodal, obtenida de la web a través del uso de la técnica de web scraping. Se detallan desde un enfoque teórico y práctico los pasos seguidos en el proceso de extracción y procesamiento de la información obtenida de www.tripadvisor.com, se genera un modelo de red social que relaciona diferentes tipos de actores dentro de la red, y se aplica un modelo para detectar de individuos influyentes propuesto anteriormente por el mismo grupo de investigación. Por último se describe la aplicación de herramientas de análisis cuantitativo a los datos obtenidos como minería de texto, frecuencia y nubes de palabras. El trabajo aborda un problema de marketing contemporáneo desde los métodos cuantitativos y la teoría de redes sociales combinando técnicas conocidas en una forma novedosa. Su resultado es el descubrimiento de información valiosa no evidente desde otros métodos de análisis.

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Citas

Alamsyah, A. & Rahardjo, B. (2021) Social Network Analysis Taxonomy Based on Graph Representation. arXiv preprint arXiv:2102.08888.

Aghdam S. M. & Navimipou N. J. (2016) Opinion leaders selection in the social networks based on trust relationships propagation. Karbala International Journal of Modern Science 2, 2016 88-97.

DOI: 10.1016/j.kijoms.2016.02.002

Aral S., Muchnik L. & Sundararajan A. (2013) Engineering social contagions: Optimal network seeding in the presence of homophily. Network Science, 1, pp 125-153

DOI: 10.1017/nws.2013.6

Awad N. & Ragowsky A. (2008) Establishing Trust in Electronic Commerce through Online Word of Mouth: An Examination across Genders. Journal of Management Information Systems, Vol. 24, No. 4, pp. 101-121

Stable URL: http://www.jstor.org/stable/40398913

Bacile T., Ye C. & Swilley E. (2014) From firm-controlled to consumer-contributed: consumer co-production of personal media marketing communication. Journal of Interactive Marketing Volume 28, Issue 2, May 2014, Pages 117–133

DOI: 10.1016/j.intmar.2013.12.001

Balkundi, P. & Kilduff, M. (2006) The ties that lead: A social approach to leadership. The Leadership Quarterly 17 (2016) 419-439

DOI: 10.1016/j.leaqua.2005.09.004

Bastian M., Heymann S., Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.

Benedetti, A. (2015) Marketing en redes sociales: Detrás de escena. Buenos Aires: Ed AMDIA

Bickart B. & Schindler R. (2001) Internet forums as influential sources of consumer information. Journal of Interactive Marketing Volume 15, Issue 3 Pages 31–40

DOI: 10.1002/dir.1014

Chen W., Wang C. & Wang C. (2010) Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale Social Networks. Proceedings KDD’10.

DOI: 10.1145/1835804.1835934

Chen X,. van der Lans R. & Phan T. Q. (2017) Uncovering the Importance of Relationship Characteristics in Social Networks: Implications for Seeding Strategies. Journal of Marketing Research: April 2017, Vol. 54, No. 2, pp. 187-201.

DOI: 10.1509/jmr.12.0511

Crawley M. (2012) The R book. 2nd ed. Wiley, 2012. Recuperado de https://www.cs.upc.edu/~robert/teaching/estadistica/TheRBook.pdf

Duan, W., Gu, B., & Whinston, A.B. (2008). Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems, 45(3), 1007–1016

DOI: 10.1016/j.dss.2008.04.001

Ekran I. & Evans C. (2016) The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior. 61, pp 47-55.

DOI: 10.1016/j.chb.2016.03.003

Gauri D., Bhatnagar A., & Rao R. (2008) Role of word of mouth in online store loyalty. Communications of the ACM Vol 51 Issue 3 pp 89-91 .

DOI: 10.1145/1325555.1325572

Grossek G., & Holotescu C. (2009) Indicators for the analysis of learning and practice communities from the perspective of microblogging as a provocative sociolect in virtual space. The 5th International Scientific Conference eLSE - eLearning and Software for Education, BUCHAREST, April 09-10, 2009

Gupte, M. & Eliassi-Rad, T. (2011). Measuring Tie Strength in Implicit Social Networks. Proceedings of the 3rd Annual ACM Web Science Conference, WebSci'12.

DOI: 10.1145/2380718.2380734.

Hansen D., Shneiderman B., & Smith M. (2011). Analyzing Social Media Networks with NodeXL: Insights from a Connected World. Ed. Morgan Kauffman.

Hewett K., Rand W., Rust R. & van Heerde H. (2016) Brand Buzz in the Echoverse. Journal of Marketing: May 2016, Vol. 80, No. 3, pp. 1-24.

DOI: 10.1509/jm.15.0033

Hawkins D., Best R., Coney K. & Carey K. (1995) Consumer behavior: Implications for marketing strategy. McGraw-Hill

Hudson S., Huang L., Roth M. S. & Madden T. (2015) The influence of social media interactions on consumer–brand relationships: A three-country study of brand perceptions and marketing behaviors. International Journal of Research in Marketing Volume 33, Issue 1, March 2016, Pages 27-41

DOI: 10.1016/j.ijresmar.2015.06.004

Jacomy M., Venturini T., Heymann S. & Bastian M. (2014) ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLoS ONE 9(6): e98679.

DOI: 10.1371/journal.pone.0098679

Katz, E. & Lazarsfeld, P.F. (1955) Personal influence: The part played by people in the flow of mass communications, New York: The Free Press.

Kempe D., Kleinberg J. & Tardos E. (2015) Maximizing the Spread of Influence through a Social Network. Theory of Computing Journal Volume 11, Article 4 pp. 105-147.

DOI: 10.4086/toc.2015.v011a004

King R., Racherla P. & Bush, V (2014) What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature, Journal of Interactive Marketing DOI: 10.1016/j.intmar.2014.02.001

Lang B. & Hyde K.(2013). Word of mouth: what we know and what we have yet to learn. Journal of consumer satisfaction, dissatisfaction and complaining behavior 26: 1–18.

Lang D. & Chien G. (2018) wordcloud2: Create Word Cloud by 'htmlwidget'. R package version 0.2.1. https://github.com/lchiffon/wordcloud2

Libai B., Muller E. & Peres R. (2013) Decomposing the Value of Word-of-Mouth Seeding Programs: Acceleration Versus Expansion. Journal of Marketing Research: April 2013, Vol. 50, No. 2, pp. 161-176.

DOI: 10.1509/jmr.11.0305

Litterio A. M., Nantes, E. A., Larrosa, J. M. & Gómez, L. J. (2017) Marketing and social networks: a criterion for detecting opinion leaders. European Journal of Management and Business Economics, Vol. 26 Issue: 3, pp.347-366.

DOI: 10.1108/EJMBE-10-2017-020

Luca, M. (2016) Reviews, Reputation, and Revenue: The Case of Yelp.com Harvard Business School, Working Paper 12-016

Ma, H. (2013) An experimental study on implicit social recommendation. SIGIR '13 Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. Pages 73-82

DOI: 10.1145/2484028.2484059

Nam H., & Kannan P. (2014) The Informational Value of Social Tagging Networks. Journal of Marketing: July 2014, Vol. 78, No. 4, pp. 21-40.

DOI: 10.1509/jm.12.0151

Nantes, E. A., Litterio, M., Larrosa, J. M. (2019). Explotación y detección de influyentes en redes sociales online implícitas. XXXIII Encuentro de Docentes Universitarios de Comercialización de Argentina y América Latina. 3 y 4 de octubre. Disponible en: https://repositoriodigital.uns.edu.ar/handle/123456789/6047

OmaymaS (2019) Webscraping Tripadvisor data: Cologne_Restaurants.R. GitHub repository, recuperado de https://github.com/OmaymaS/Web-Scraping-TripAdvisor-Data-/blob/master/Cologne_Restaurants.Ren junio de 2019.

Pavlou P. & Ba S. (2002) Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premium and Buyer Behavior. MIS Quarterly 26, 3, pp 243-268.

DOI: 10.2307/4132332

Serrano Puche J. (2016) Internet y emociones. Nuevas tendencias en un campo de investigación emergente. Comunicar: Revista científica iberoamericana de comunicación y educación, Nº 46, pp 19-26.

DOI: 10.3916/C46-2016-02

Silge J. & Robinson D. (2016) tidytext: Text Mining and Analysis Using Tidy Data Principles in R. Journal of Open Source Software, 1(3), 37

DOI 10.21105/joss.00037

Smith M., L. Rainie, I. Himelboim & B. Shneiderman (2014), Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Center.

Peters K., Chen Y., Kaplan A., Ognibeni B. & Pauwels K. (2013) Social media metrics - A framework and guidelines for managing social media. Journal of Interactive Marketing Vol 27 pp 281-298

DOI: 10.1016/j.intmar.2013.09.007

R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Versión 3.6. URL https://www.R-project.org/.

RStudio Team (2018). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/.

Simon H. A. (1982) Models of bounded rationality: Empirically grounded economic reason (Vol. 3). MIT press.

Tripadvisor US Press Center (2020) Recuperado de https://tripadvisor.mediaroom.com/ el 25/02/2020.

Van der Merwe R. & van Heerden G. (2009) Finding and utilizing opinion leaders: Social networks and the power of relationships. South African Journal of Business Management, Vol. 40, pp 65-76.

VanMeter R., Grisaffe D. & Chonko L. (2015) Of “Likes” and “Pins”: The Effects of Consumers' Attachment to Social Media. Journal of Interactive Marketing 32, pp 70-88.

DOI: 10.1016/j.intmar.2015.09.001

Wang Z. & Gon Kim H. (2017) Can Social Media Marketing Improve Customer Relationship Capabilities and Firm Performance? Dynamic Capability Perspective. Journal of Interactive Marketing. Volume 39, August 2017, Pages 15–26

DOI: 10.1016/j.intmar.2017.02.004

Wickham H. (2019) rvest: Easily Harvest (Scrape) Web Pages. http://rvest.tidyverse.org/, https://github.com/tidyverse/rvest.

Zhou T., Ren J., Medo M. & Zhang Y. (2007) Bipartite network projection and personal recommendation. Physical review. E, Statistical, nonlinear, and soft matter physics. 76. 046115.

DOI: 10.1103/PhysRevE.76.046115.

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Publicado

2022-10-26

Cómo citar

Litterio, A. M., Nantes, E. A., & Larrosa, J. M. (2022). La influencia en redes sociales online bimodales a través del caso de tripadvisor . Informes De Investigacion. IIATA., 7(7), 225 - 253. https://doi.org/10.35305/iiata.v7i7.89

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