Skip to main content
Log in

Behavioral and Migration Analysis of the Dynamic Customer Relationships on Twitter

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

Relationship management has been of strategic importance for businesses that are interested to evaluate the state of the relationship with the customer and if possible to migrate customers to better and more binding states. This work addresses the problem of estimating the relationship state of a customer and examining the migration policy of the customer, using social media analytics. We propose an innovative framework, where clustering, linguistic and emotional analytics are used to automatically assign users to relationship states. Our research is of multi-disciplinary nature, where we are using existing results from surveys on users’ behavior when mitigating states to verify the semantics of our metrics, showing that they follow similar behavior. Our results show that clustering users based on communication, emotions and perceived product mix can result in an automated assignment of users to states. Furthermore, trust, commitment and homophily are defined and our results show that users are migrating states influenced by these values. Our work provides data analytics metrics for businesses that will identify and address the problem of relationship management thus improving the overall users’ satisfaction using a data analytics approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://cran.r-project.org/web/packages/twitteR

  2. https://developer.twitter.com/

  3. https://cran.r-project.org/web/packages/sentimentr/

  4. https://cran.r-project.org/web/packages/cluster/

  5. https://github.com/data-science-virtual-lab

References

  • Adali, S., Escriva, R., Goldberg, M.K., Hayvanovych, M., Magdon-Ismail, M., Szymanski, B.K., Wallace, W.A., & Williams, G. (2010). Measuring behavioral trust in social networks. In 2010 IEEE international conference on intelligence and security informatics (pp. 150–152): IEEE.

  • Amelio, A., & Tagarelli, A. (2018). Silhouette for the evaluation of community structures in multiplex networks. In International workshop on complex networks (pp. 41–49): Springer.

  • Bagozzi, R.P., Gopinath, M., & Nyer, P.U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184–206.

    Article  Google Scholar 

  • Calderon, N.A., Fisher, B., Hemsley, J., Ceskavich, B., Jansen, G., Marciano, R., & Lemieux, V.L. (2015). Mixed-initiative social media analytics at the world bank: observations of citizen sentiment in twitter data to explore“ trust” of political actors and state institutions and its relationship to social protest. In 2015 IEEE international conference on Big Data (Big Data) (pp. 1678–1687): IEEE.

  • Chambers, J.M. (2018). Graphical methods for data analysis. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Chatzakou, D., Koutsonikola, V., Vakali, A., & Kafetsios, K. (2013). Micro-blogging content analysis via emotionally-driven clustering. In 2013 humaine association conference on affective computing and intelligent interaction (pp. 375–380): IEEE.

  • Choudhury, M.M., & Harrigan, P. (2014). Crm to social crm:the integration of new technologies into customer relationship management. Journal of Strategic Marketing, 22(2), 149–176.

    Article  Google Scholar 

  • Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? predicting political orientation and measuring political homophily in twitter using big data. Journal of Communication, 64 (2), 317–332.

    Article  Google Scholar 

  • Cruz, R.A.B., & Lee, H.J. (2014). The brand personality effect: communicating brand personality on twitter and its influence on online community engagement. Journal of Intelligence and Information Systems, 20(1), 67–101.

    Article  Google Scholar 

  • De Choudhury, M. (2011). Tie formation on twitter: Homophily and structure of egocentric networks. In 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing (pp. 465–470): IEEE.

  • Dong, R., Li, L., Zhang, Q., & Cai, G. (2018). Information diffusion on social media during natural disasters. IEEE Transactions on Computational Social Systems, 5(1), 265–276.

    Article  Google Scholar 

  • Hamdi, S., Gancarski, A.L., Bouzeghoub, A., & Yahia, S.B. (2016). Tison: Trust inference in trust-oriented social networks. ACM Transactions on Information Systems (TOIS), 34(3), 1–32.

    Article  Google Scholar 

  • Kafeza, E., Kanavos, A., Makris, C., Pispirigos, G., & Vikatos, P. (2019). T-pcce: Twitter personality based communicative communities extraction system for big data. In IEEE transactions on knowledge and data engineering.

  • Kafeza, E., Makris, C., & Rompolas, G. (2017). Exploiting time series analysis in twitter to measure a campaign process performance. In 2017 IEEE international conference on Services Computing (SCC) (pp. 68–75): IEEE.

  • Kamvar, S.D., Schlosser, M.T., & Garcia-Molina, H. (2003). The eigentrust algorithm for reputation management in p2p networks. In Proceedings of the 12th international conference on World Wide Web (pp. 640–651).

  • Kanavos, A., Kafeza, E., & Makris, C. (2015). Can we rank emotions? a brand love ranking system for emotional terms. In 2015 IEEE international congress on big data (pp. 71–78): IEEE.

  • Kaufman, L., & Rousseeuw, P.J. (2009). Finding groups in data: an introduction to cluster analysis, Vol. 344, Wiley, New York.

  • Kayes, I., & Chakareski, J. (2015). Retention in online blogging:, a case study of the blogster community. IEEE Transactions on Computational Social Systems, 2(1), 1–14.

    Article  Google Scholar 

  • Kim, E., Sung, Y., & Kang, H. (2014). Brand followers’ retweeting behavior on twitter:, How brand relationships influence brand electronic word-of-mouth. Computers in Human Behavior, 37, 18–25.

    Article  Google Scholar 

  • Kiritchenko, S., Zhu, X., & Mohammad, S.M. (2014). Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50, 723–762.

    Article  Google Scholar 

  • Liang, G., He, W., Xu, C., Chen, L., & Zeng, J. (2015). Rumor identification in microblogging systems based on users’ behavior. IEEE Transactions on Computational Social Systems, 2(3), 99–108.

    Article  Google Scholar 

  • McPherson, M., Smith-Lovin, L., & Cook, J.M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.

    Article  Google Scholar 

  • Mohammad, S.M., & Turney, P.D. (2010). Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text. Association for Computational Linguistics (pp. 26–34).

  • Mohammad, S.M., & Turney, P.D. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436–465.

    Article  Google Scholar 

  • Motiwalla, L., Deokar, A.V., Sarnikar, S., & Dimoka, A. (2019). Leveraging data analytics for behavioral research. Information Systems Frontiers, 21(4), 735–742.

    Article  Google Scholar 

  • Nguyen, T.T., Harper, F.M., Terveen, L., & Konstan, J.A. (2018). User personality and user satisfaction with recommender systems. Information Systems Frontiers, 20(6), 1173–1189.

    Article  Google Scholar 

  • Phua, J., Jin, S.V., & Kim, J.J. (2017). Gratifications of using facebook, twitter, instagram, or snapchat to follow brands:, The moderating effect of social comparison, trust, tie strength, and network homophily on brand identification, brand engagement, brand commitment, and membership intention. Telematics and Informatics, 34(1), 412–424.

    Article  Google Scholar 

  • Rousseeuw, P.J. (1987). Silhouettes:, a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.

    Article  Google Scholar 

  • Ryals, L., & Knox, S. (2001). Cross-functional issues in the implementation of relationship marketing through customer relationship management. European Management Journal, 19(5), 534–542.

    Article  Google Scholar 

  • Staab, S., Bhargava, B., Leszek, L., Rosenthal, A., Winslett, M., Sloman, M., Dillon, T.S., Chang, E., Hussain, F., Nejdl, W., & et al. (2004). The pudding of trust. IEEE Intelligent Systems, 19(5), 74–88.

    Article  Google Scholar 

  • Tavakolifard, M., Almeroth, K.C., & Gulla, J.A. (2013). Does social contact matter? modelling the hidden web of trust underlying twitter. In Proceedings of the 22nd international conference on world wide web (pp. 981–988).

  • Turri, A.M., Smith, K.H., & Kemp, E. (2013). Developing affective brand commitment through social media. Journal of Electronic Commerce Research, 14, 3.

    Google Scholar 

  • Uddin, M.M., Imran, M., & Sajjad, H. (2014). Understanding types of users on twitter. arXiv:1406.1335.

  • Zadeh, A.H., & Sharda, R. (2014). Modeling brand post popularity dynamics in online social networks. Decision Support Systems, 65, 59–68.

    Article  Google Scholar 

  • Zhang, J.Z., Watson Iv, G.F., Palmatier, R.W., & Dant, R.P. (2016). Dynamic relationship marketing. Journal of Marketing, 80(5), 53–75.

    Article  Google Scholar 

  • Zheng, C., Yu, X., & Jin, Q. (2017). How user relationships affect user perceived value propositions of enterprises on social commerce platforms. Information Systems Frontiers, 19(6), 1261–1271.

    Article  Google Scholar 

  • Zhou, R., & Hwang, K. (2007). Powertrust:, A robust and scalable reputation system for trusted peer-to-peer computing. IEEE Transactions on Parallel and Distributed Systems, 18(4), 460–473.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eleanna Kafeza.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This research was supported by the Research Incentive Fund (RIF) Grants R17059 and R18087 provided by Zayed University, UAE.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kafeza, E., Makris, C., Rompolas, G. et al. Behavioral and Migration Analysis of the Dynamic Customer Relationships on Twitter. Inf Syst Front 23, 1303–1316 (2021). https://doi.org/10.1007/s10796-020-10033-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-020-10033-4

Keywords

Navigation