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Content-Based Analytics of Diffusion on Social Big Data: A Case Study on Korean Telecommunication Companies

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Scalable Information Systems (INFOSCALE 2014)

Abstract

Social networking services have been playing an important role of communicating with customers. Particularly, firms seek to deploy Twitter for the benefit of their business because it has rapidly become an information vehicle for consumers who are disseminating information on products and services. Thus, this study examines how information shared by firms is diffused and what the important factors in understanding information dissemination are. Specially, this study classifies the types of tweets posted by a firm (@olleh_mobile) and then to investigate the effect of these types of tweets on diffusion. By using content analysis, this study defined two categories (‘Information providing’ and ‘Advertisement’ type) and eight subordinate concepts (News, Usage, Preview, Notice, Sale, Benefit, Event, Service public relations). These results indicate that the differences are significant for all three types of information content. It shows that firms can spread information more quickly by providing the ‘Information and advertisement’ type rather than the ‘Advertisement’ type.

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References

  • Bajwa, D.S., Lewis, L.F., Pervan, G., Lai, V.S., Munkvold, B.E., Schwabe, G.: Factors in the global assimilation of collaborative information technologies: an exploratory investigation in five regions. J. Manag. Inf. Syst. 25(1), 131–165 (2008)

    Article  Google Scholar 

  • Barnes, S.J., BöHringer, M.: Modeling use continuance behavior in microblogging services: the case of twitter. J. Comput. Inf. Syst. 51(1), 1–10 (2011)

    Google Scholar 

  • Berinato, S.: Six ways to find value in twitter’s noise. Harvard Bus. Rev. 88(6), 34–35 (2010)

    Google Scholar 

  • Boettger, R.K., Palmer, L.A.: Quantitative content analysis: its use in technical communication. IEEE Trans. Prof. Commun. 53(4), 346–357 (2010)

    Article  Google Scholar 

  • Brancheau, J.C., Wetherbe, J.C.: The adoption of spreadsheet software: testing innovation diffusion theory in the context of end-user computing. Inf. Syst. Res. 1(2), 115–143 (1990)

    Article  Google Scholar 

  • Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: The million follower fallacy. In: Association for the Advancement of Artificial Intelligence (2010)

    Google Scholar 

  • Chatman, E.A.: Diffusion theory: a review and test of a conceptual model in information diffusion. J. Am. Soc. Inf. Sci. 37(6), 377–386 (1986)

    Article  Google Scholar 

  • Cheung, C., M.K, Lee, M.K.O., Rabjohn, N.: The impact of electronic word-of-mouth. The adoption of online opinions in online customer communities. Int. Res. 18(3), 229-247 (2008)

    Google Scholar 

  • Fichman, R.G.: Information technology diffusion: a review of empirical research. In: Proceedings of the 13th International Conference on Information Systems (1992)

    Google Scholar 

  • Fischer, E., Reuber, A.R.: Social interaction via new social media: (how) can interactions on twitter affect effectual thinking and behavior? J. Bus. Ventur. 26(1), 1–18 (2011)

    Article  Google Scholar 

  • Fullwood, C., Sheehan, N., Nicholls, W.: Blog function revisited: a content analysis of myspace blogs. CyberPsychol. Behav. 12(6), 685–689 (2009)

    Article  Google Scholar 

  • Greer, C.F., Ferguson, D.A.: Using twitter for promotion and branding: a content analysis of local television twitter sites. J. Broadcast. Electron. Media. 55(2), 198–214 (2011)

    Article  Google Scholar 

  • Ha, L., James, E.L.: Interactivity reexamined: a baseline analysis of early business web sites. J. Broadcast. Electron. Media. 42, 457–474 (1998)

    Article  Google Scholar 

  • Herring, S.C., Scheidt, L.A., Kouper, I., Wright, E.: A Longitudinal Content Analysis of Weblogs: 2003-2004. Routledge, London (2004)

    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 

  • Java, A., Song, T.F., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the Ninth WEBKDD and First SNA-KDD Workshop on Web Mining and Social Network Analysis, pp. 56–65 (2007)

    Google Scholar 

  • Kaplan, A.M., Haenlein, M.: Users of the world, unite! the challenges and opportunities of social media. Bus. Horiz. 53(1), 59–68 (2010)

    Article  Google Scholar 

  • Kim, H., Son, I., Lee, D.: The viral effect of online social network on new products promotion: investigating information diffusion on twitter. J. Intell. Inf. Syst. 18(2) (2012)

    Google Scholar 

  • Kruskal, W.H., Wallis, W.A.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47(260), 583–621 (1957)

    Article  Google Scholar 

  • Li, J., Rao, H.R.: Twitter as a rapido response news service: an exploration in the context of the 2008 china earthquake. Electron. J. Inf. Syst. Dev. Countries. 42(4), 1–22 (2010)

    Google Scholar 

  • Lin, J.-S., Pena, J.: Are you following me? a content analysis of tv networks’ brand communication on twitter’ the purpose of using twitter. J. Interact. Advertising. 12(1), 17–29 (2011)

    Article  Google Scholar 

  • Miaskiewicz, T., Monarchi, D.E.: A review of the literature on the empathy construct using cluster analysis. Commun. AIS 22, pp. 117–142 (2008)

    Google Scholar 

  • Morris, R.: Computerized content analysis in management research: a demonstration of advantages and limitations. J. Manag. 20(4), 903–931 (1994)

    Google Scholar 

  • Naaman, M., Boase, J., Lai, C.-H.: Is it really about me? Message content in social awareness streams. In: CSCW (2010)

    Google Scholar 

  • Papacharissi, Z.: The blogger revolution? audiences as media producers. In: The Annual Conference of the International Communication Association, New Orleans (2004)

    Google Scholar 

  • Punj, G., Stewart, D.W.: Cluster analysis in marketing research: review and suggestions for application. J. Mark. Res. 20, 134–148 (1983)

    Article  Google Scholar 

  • Rogers, E.M.: The Diffusion of Innovations. Free Press, New York (1983)

    Google Scholar 

  • Rogers, E.M.: Diffusion of Innovations. Free Press, New York (2003)

    Google Scholar 

  • Rogers, E.M., Shoemaker, F.F.: New York: Free Press (1971)

    Google Scholar 

  • Savage, N.: Twitter as medium and message. Commun. ACM 54(3), 18–20 (2011)

    Article  Google Scholar 

  • Segars, A.H., Grover, V.: Profiles of strategic information systems planning. Inf. Syst. Res. 10(3), 199 (1999)

    Google Scholar 

  • Simmons, L.L., Conlon, S., Mukhopadhyay, S., Yang, J.: A computer aided content analysis of online reviews. J. Comput. Inf. Syst. 52, 43–55 (2011)

    Google Scholar 

  • Sledgianowski, D., Kulviwat, S.: Using social network sites: the effects of playfulness, critical mass and trust in a hedonic context. J. Comput. Inf. Syst. 49(4), 74–83 (2009)

    Google Scholar 

  • Smith, B.G.: Socially distributing public relations: twitter, haiti, and interactivity in social media. Pub. Relat. Rev. 36(4), 329–335 (2010)

    Article  Google Scholar 

  • Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment in twitter events. J. Am. Soc. Inf. Sci. Technol. 62(2), 406–418 (2011)

    Article  Google Scholar 

  • Yang, J., Counts, S.: Predicting the speed, scale, and range of information diffusion in twitter. In: 4th International AAAI Conference on Weblogs and Social Media (ICWSM) (2010)

    Google Scholar 

  • Zhang, W., Watts, S.A.: Capitalizing on content: information adoption in two online communities. J. Assoc. Inf. Syst. 9(2), 72–93 (2008)

    Google Scholar 

  • Zhao, D., Rosson, M.B.: How and why people twitter: the role that micoblogging plays in informal communication at work. In: Proceedings of the ACM 2009 International Conference on Supporting Group Work, Sanibel Island, FL, pp. 243–252 (2009)

    Google Scholar 

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Acknowledgement

This research was supported by the MSIP (Ministry of Science, ICT&Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2014-H0301-14-1044) supervised by the NIPA (National ICT Industry Promotion Agency).

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Correspondence to Jason J. Jung .

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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Lee, N., Jung, J.J. (2015). Content-Based Analytics of Diffusion on Social Big Data: A Case Study on Korean Telecommunication Companies. In: Jung, J., Badica, C., Kiss, A. (eds) Scalable Information Systems. INFOSCALE 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 139. Springer, Cham. https://doi.org/10.1007/978-3-319-16868-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-16868-5_2

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