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Clustering of Social Media Data and Marketing Decisions

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Digital Economy. Emerging Technologies and Business Innovation (ICDEc 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 395))

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Abstract

The technological revolution and the appearance of Social Media have made it possible to generate large volumes of heterogeneous data called Big Data. Today, Big Data Analytics plays a very important role for businesses in making marketing decisions. Social Media Data represents a large part of Big Data and are characterized by complex and unstructured formats, which makes their analysis a difficult task. The challenge for researchers and decision-makers is to find a path to facilitate the analysis of these huge data in order to extract relevant information and to improve marketing decisions and strategies. In this context, previous research proposed several methods and techniques such as Data Mining, visualization and machine learning. Data Mining techniques are among the most widely used techniques and include Clustering techniques. Clustering provides a wide range of techniques that classify unstructured data and detect useful knowledge from large data sets. In this regard, numerous articles on analyzing Social Media Data using Clustering have been published and there has been a rapid increase in the number of publications in the areas of Social Media Data and marketing, in which several Clustering methods have been proposed. Despite this increase, there is a lack of articles organizing these publications according to Clustering techniques and added value. The aim of this paper is to answer the following questions: What are the techniques for aggregating Social Media Data? What are the marketing decisions generated by Social Media Data Clustering? Thus, it will be useful to present a review and a classification of research articles on Social Media Analysis in the field of marketing using Clustering to provide an overview to researchers and managers looking to use these techniques.

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References

  • Afolabi, I.T., Ezenwoke, A.A., Ayo, C.K.: Competitive analysis of social media data in the banking industry. Int. J. Internet Mark. Advertising 11(3), 183 (2017)

    Article  Google Scholar 

  • Alavion, S.J., Taghdisi.: A. rural e-marketing in Iran; modeling villagers’ intention and clustering rural regions. Inf. Process. Agric. (2020). https://doi.org/10.1016/j.inpa.2020.02.008

  • Amaro, S., Duarte, P., Henriques, C.: Travelers’ use of social media: a clustering approach. Ann. Tour. Res. 59, 1–15 (2016). https://doi.org/10.1016/j.annals.2016.03.007

  • Bach Tobji, M.A., Jallouli, R., Koubaa, Y., Nijholt, A.: Digital Economy: Emerging Technologies and Business Innovation. LNBIP, vol. 325. Springer, Heidelberg (2018). ISBN 978-3-319-97748-5

    Book  Google Scholar 

  • Bardin: L’analyse de contenu, 2ème Edition Puf, Quadrillage Manuels (2009)

    Google Scholar 

  • Bello-Orgaz, G., Mesas, R.M., Zarco, C., Rodriguez, V., Cordón, O., Camacho, D.: Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter. Inf. Process. Manag. (2020. Article in Press. https://doi.org/10.1016/j.ipm.2020.102220

  • Cukier, K.: The economist, data everywhere: a special report on managing information. Economist 394, 3–5 (2010)

    Google Scholar 

  • Galetsi, P., Katsaliaki, K., Kumar, S.: Big data analytics in health sector: theoretical framework, techniques and prospects. Int. J. Inf. Manag. 50, 206–216 (2020)

    Article  Google Scholar 

  • Ghani, N.A., Hamid, S., Targio Hashem, I.A., Ahmed, E.: Big social media data analytics: a survey. Comput. Hum. Behav. (2018). https://doi.org/10.1016/j.chb.2018.08.039

  • Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems - CHI 2009 (2009). https://doi.org/10.1145/1518701.1518736

  • Gitto, S., Mancuso, P.: Brand perceptions of airports using social networks. J. Air Transp. Manag. 75, 153–163 (2019). https://doi.org/10.1016/j.jairtraman.2019.01.010

    Article  Google Scholar 

  • Hasnat, M.M., Hasan, S.: Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data. Transp. Res. Part C: Emerg. Technol. 96, 38–54 (2018)

    Article  Google Scholar 

  • Jabbar, A., Akhtar, P., Dani, S.: Real-time big data processing for instantaneous marketing decisions: a problematization approach. Ind. Mark. Manag. (2019). https://doi.org/10.1016/j.indmarman.2019.09.001

  • Jallouli, R., Bach Tobji, M.A., Bélisle, D., Mellouli, S., Abdallah, F., Osman, I.H.: Digital Economy. Emerging Technologies and Business Innovation. LNBIP, vol. 358. Springer, Heidelberg (2019). ISBN 978-3-030-30873-5

    Book  Google Scholar 

  • Jallouli, R., Nasraoui, O., Bach Tobji, M.A., Srarfi Tabbane, R., Rhouma, R.: International Conference on Digital Economy, ICDEc 2016, Carthage, Tunisia, 28–30 April 2016. IEEE (2016). ISBN 978-1-5090-2230-4

    Google Scholar 

  • Jallouli, R., Zaïane, O.R., Bach Tobji, M.A., Srarfi Tabbane, R., Nijholt, A.: Digital Economy: Emerging Technologies and Business Innovation. LNBIP, vol. 290. Springer, Heidelberg (2017). ISBN 978-3-319-62736-6

    Book  Google Scholar 

  • Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: tweets as electronic word of mouth. J. Am. Soc. Inform. Sci. Technol. 60(11), 2169–2188 (2009)

    Article  Google Scholar 

  • Jimenez-Marquez, J.L., Gonzalez-Carrasco, I., Lopez-Cuadrado, J.L., Ruiz-Mezcua, B.: Towards a big data framework for analyzing social media content. Int. J. Inf. Manag. 44, 1–12 (2019)

    Article  Google Scholar 

  • Jisun, A., Haewoon, K., Bernard, J.J.: Automatic generation of personas using youtube social media data. In: Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

    Google Scholar 

  • Kaabi, S., Jallouli, R.: Overview of e commerce technologies, data analysis capabilities and marketing knowledge. In: International Conference on Digital Economy ICDEc 2019, 15–18, Beirut, Lebanon, 12 p (2019)

    Google Scholar 

  • Kang, H.-N., Yong, H.-R., Hwang, H.-S.: Brand clustering based on social big data: a case study. Int. J. Softw. Eng. Appl. 10 (4), 27–36 (2016). https://doi.org/10.14257/ijseia.2016.10.4.03

  • Katal, A., Wazid, M., Goudar, R.H.: Big data: issues, challenges, tools and good practices. In: 2013 Sixth International Conference on Contemporary Computing (IC3) (2013). https://doi.org/10.1109/ic3.2013.6612229

  • Kim, J., Hastak, M.: Social network analysis. Int. J. Inf. Manag. J. Inf. Prof. 38(1), 86–96 (2018)

    Article  Google Scholar 

  • Kirilenko, A.P., Stepchenkova, S.O., Hernandez, J.M.: Comparative clustering of destination attractions for different origin markets with network and spatial analyses of online reviews. Tour. Manag. 72, 400–410 (2019)

    Article  Google Scholar 

  • Koubaa, H., Jallouli, R.: Social networks and societal strategic orientation in the hotel sector: netnographic study. In: International Conference on Digital Economy (2019)

    Google Scholar 

  • Laney, D.: 3D Data Management: Controlling Data Volume, Velocity, and Variety. META group Inc., 2001 (2013). http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf

  • Lankton, N.K., McKnight, D.H., Tripp, J.F.: Facebook privacy management strategies: a cluster analysis of user privacy behaviors. Comput. Hum. Behav. 76, 149–163 (2017). https://doi.org/10.1016/j.chb.2017.07.015

  • Ianni, M., Masciari, E., Mazzeo, G.M., Mezzanzanica, M., Zaniolo, C.: Fast and effective big data exploration by clustering. Future Gener. Comput. Syst. 102, 84–94 (2019). https://doi.org/10.1016/j.future.2019.07.077

    Article  Google Scholar 

  • Lin, S., Yang, S., Ma, M., Huang, J.: Value co-creation on social media. Int. J. Contemp. Hospital. Manag. 30(4), 2153–2174 (2018). https://doi.org/10.1108/ijchm-08-2016-0484

  • Liu, X., Shin, H., Burns, A.C.: Examining the impact of luxury brand’s social media marketing on customer engagement: using big data analytics and natural language processing. J. Bus. Res. (2019). https://doi.org/10.1016/j.jbusres.2019.04.042

  • Lu, X.S., Zhou, M., Qi, L., Liu, H.: Clustering-algorithm-based rare-event evolution analysis via social media data. IEEE Trans. Comput. Soc. Syst. 1–10 (2019). https://doi.org/10.1109/tcss.2019.2898774

  • Lyu, K., Kim, H.: Sentiment analysis using word polarity of social media. Wirel. Pers. Commun. 89(3), 941–958 (2016)

    Article  Google Scholar 

  • Misirlis, N., Vlachopoulou, M.: Social media metrics and analytics in marketing – S3M: a mapping literature review. Int. J. Inf. Manag. 38(1), 270–276 (2018). https://doi.org/10.1016/j.ijinfomgt.2017.10.005

  • Moed, H.F., Bar-Ilan, J., Halevi, G.: A new methodology for comparing Google Scholar and Scopus. J. Informetrics 10(2), 533–551 (2016). https://doi.org/10.1016/j.joi.2016.04.017

    Article  Google Scholar 

  • Mostapha, M.: Clustering halal food consumers: a Twitter sentiment analysis. Int. J. Mark. Res. 1–18 (2018). https://doi.org/10.1177/1470785318771451

  • Oliveira, L., Figueira, A.: Improving the benchmarking of social media content strategies using clustering and KPI. Procedia Comput. Sci. 121, 826–834 (2017)

    Article  Google Scholar 

  • Pournarakis, D.E., Sotiropoulos, D.N., Giaglis, G.M.: A computational model for mining consumer perceptions in social media. Decis. Support Syst. 93, 98–110 (2017). https://doi.org/10.1016/j.dss.2016.09.018

    Article  Google Scholar 

  • Punel, A., Ermagun, A.: Using Twitter network to detect market segments in the airline industry. J. Air Transp. Manag. 73, 67–76 (2018). https://doi.org/10.1016/j.jairtraman.2018.08.004

  • Ramasubbareddy, S., Srinivas, T.A.S., Govinda, K., Manivannan, S.S.: Comparative study of clustering techniques in market segmentation. Innov. Comput. Sci. Eng. 103, 117–125 (2020)

    Article  Google Scholar 

  • Rathore, A.K., Ilavarasan, P.V.: Pre- and post-launch emotions in new product development: Insights from twitter analytics of three products. Int. J. Inf. Manag. 50, 111–127 (2020)

    Article  Google Scholar 

  • ur Rehman, M.H., Chang, V., Batool, A., Wah, T.Y.: Big data reduction framework for value creation in sustainable enterprises. Int. J. Inf. Manag. 36(6), 917–928 (2016)

    Google Scholar 

  • Robert, A.D., Bouillaguet, A.: L’analyse de contenu. Presses universitaires de France (1997). Sautter, E.T., Leisen, B.: Managing stakeholders a tourism planning model (1999)

    Google Scholar 

  • Saggi, M.K., Jain, S.: A survey towards an integration of big data analytics to big insights for value-creation. Inf. Process. Manag. 54(5), 758–790 (2018). https://doi.org/10.1016/j.ipm.2018.01.010

  • Singh, A., Shukla, N., Mishra, N.: Social media data analytics to improve supply chain management in food industries. Transp. Res. Part E: Logist. Transp. Rev. 114, 398–415 (2017)

    Article  Google Scholar 

  • Stamatelatos, G., Gyftopoulos, S., Drosatos, G., Efraimidis, P.S.: Revealing the political affinity of online entities through their Twitter followers. Inf. Process. Manag. 57(2) (2020)

    Google Scholar 

  • Stieglitz, S., Mirbabaie, M., Ross, B., Neuberger, C.: Social media analytics – challenges in topic discovery, data collection, and data preparation. Int. J. Inf. Manag. 39, 156–168 (2018)

    Article  Google Scholar 

  • Terzi, B., Bulut, S., Kaya, N.: Factors affecting nursing and midwifery students’ attitudes toward social media. Nurse Educ. Pract. 35, 141–149 (2019). https://doi.org/10.1016/j.nepr.2019.02.012

    Article  Google Scholar 

  • Van Dieijen, M., Borah, A., Tellis, G.J., Franses, P.H.: Big data analysis of volatility spillovers of brands across social media and stock markets. Ind. Mark. Manag. (2019). https://doi.org/10.1016/j.indmarman.2018.12.006

    Article  Google Scholar 

  • Wang, A., Gao, X.: Hybrid variable-scale clustering method for social media marketing on user generated instant music video. Tech. Gazette 26(3) (2019). https://doi.org/10.17559/tv-20190314152108

  • Wu, X., Zhu, X., Wu, G.-Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)

    Article  Google Scholar 

  • Yang, Y., See-To, E.W.K., Papagiannidis, S.: You have not been archiving emails for no reason! Using big data analytics to cluster B2B interest in products and services and link clusters to financial performance. Ind. Mark. Manag. (2019). https://doi.org/10.1016/j.indmarman.2019.01.016

    Article  Google Scholar 

  • Zhang, Z., He, Q., Zhu, S.: Potentials of using social media to infer the longitudinal travel behavior: a sequential model- based clustering method. Transp. Res. Part C: Emerg. Technol. 85, 396–414 (2017). https://doi.org/10.1016/j.trc.2017.10.005

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Manouba, Manouba, Tunisia

    Teissir Benslama & Rim Jallouli

Authors
  1. Teissir Benslama
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  2. Rim Jallouli
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Correspondence to Teissir Benslama .

Editor information

Editors and Affiliations

  1. University of Manouba, Manouba, Tunisia

    Mohamed Anis Bach Tobji

  2. University of Manouba, Manouba, Tunisia

    Rim Jallouli

  3. Institut National des Sciences Appliqué, Strasbourg, France

    Ahmed Samet

  4. NEOMA Business School, Mont-Saint-Aignan, France

    Mourad Touzani

  5. Bucharest Business School, Bucharest, Romania

    Vasile Alecsandru Strat

  6. Bucharest University of Economic Studies, Bucharest, Romania

    Paul Pocatilu

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Benslama, T., Jallouli, R. (2020). Clustering of Social Media Data and Marketing Decisions. In: Bach Tobji, M.A., Jallouli, R., Samet, A., Touzani, M., Strat, V.A., Pocatilu, P. (eds) Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2020. Lecture Notes in Business Information Processing, vol 395. Springer, Cham. https://doi.org/10.1007/978-3-030-64642-4_5

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  • DOI: https://doi.org/10.1007/978-3-030-64642-4_5

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