nach oben

Erschienen in:

2016 | OriginalPaper | Buchkapitel

Insights from Twitter Analytics: Modeling Social Media Personality Dimensions and Impact of Breakthrough Events

verfasst von : Akshat Lakhiwal, Arpan Kumar Kar

Erschienen in: Social Media: The Good, the Bad, and the Ugly

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Social media and big data have been in high focus due to their potentially huge impact on business, society and polity. This research contributes to the same domain and peruses the twitter community before and after an event which is a major breakthrough for an economy. Here, the event being monitored is the Union Budget-2016 in India. The research taps the occasion to understand the various groups which participate in the online discussion amongst 43,924 tweets from 22,896 users and the pre and post budget twitter metrics are analyzed, deducing the sensitivity of the groups to the day of proposal of the budget. The research framework incorporates twitter analytics and relies on visual and quantitative data, drawing inferences from the intelligence. How the personality dimensions change before and after the event, is also analyzed. This change in dimensions can directly account for the influencing nature of the social media group.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Discourse Analysis of Blogs: Analyzing Language to Maximize the Value of Consumption-Oriented Blogs as Data Source

Nächstes Kapitel Analysis of the Value of Public Geotagged Data from Twitter from the Perspective of Providing Situational Awareness

Aral, S., Dellarocas, C., Godes, D.: Social media and business transformation: a framework for research. Inf. Syst. Res. 24, 3–13 (2013)CrossRef

Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., Mozetič, I.: The effects of Twitter sentiment on stock price returns. PLoS ONE 10(9), e0138441 (2015)CrossRef

Bruns, A., Liang, Y.E.: Tools and methods for capturing Twitter data during natural disasters. First Monday 17(4), 1–8 (2012)CrossRef

Makice, K.: Twitter API: Up and Running: Learn How to Build Applications with the Twitter API. O’Reilly Media, Inc., Sebastopol (2009)

Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web. ACM (2010)

Kouloumpis, E., Wilson, T., Moore, T.D.: Twitter sentiment analysis: the good the bad and the OMG!. In: ICWSM 2011, pp. 538–541 (2011)

Aaker, J.L.: Dimensions of brand personality. J. Market. Res. 34, 347–356 (1997)CrossRef

O’reilly, T.: What is Web 2.0: design patterns and business models for the next generation of software. Commun. Strat. 1, 17 (2007)

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

10.

Kietzmann, J.H., et al.: Social media? Get serious! Understanding the functional building blocks of social media. Bus. Horiz. 54(3), 241–251 (2011)CrossRef

11.

Bruns, A., Stieglitz, S.: Towards more systematic Twitter analysis: metrics for tweeting activities. Int. J. Soc. Res. Methodol. 16(2), 91–108 (2013)CrossRef

12.

Chae, B.K.: Insights from hashtag# supplychain and Twitter analytics: considering Twitter and Twitter data for supply chain practice and research. Int. J. Prod. Econ. 165, 247–259 (2015)CrossRef

13.

Gurstein, M.B.: Open data: empowering the empowered or effective data use for everyone? FirstMonday 16(2) (2011)

14.

Thelwall, M.: Heart and soul: sentiment strength detection in the social web with SentiStrength. In: Proceedings of the CyberEmotions, pp. 1–14 (2013)

15.

Caprara, G.V., Barbaranelli, C., Guido, G.: Brand personality: how to make the metaphor fit? J. Econ. Psychol. 22(3), 377–395 (2001)CrossRef

16.

Geuens, M., Weijters, B., De Wulf, K.: A new measure of brand personality. Int. J. Res. Market. 26(2), 97–107 (2009)CrossRef

17.

Glynn, M.W., Faulds, D.J.: Social media: the new hybrid element of the promotion mix. Bus. Horiz. 52(4), 357–365 (2009)CrossRef

18.

Clifton, B.: Advanced web metrics with Google Analytics. Wiley, New York (2012)

19.

Ekinci, Y., Hosany, S.: Destination personality: an application of brand personality to tourism destinations. J. Travel Res. 45(2), 127–139 (2006)CrossRef

20.

Kar, A.K.: A group decision support system for selecting an open source tool for social media integration. In: Sengupta, S., Das, K., Khan, G. (eds.) Emerging Trends in Computing and Communication. Lecture Notes in Electrical Engineering, vol. 298, pp. 407–413. Springer, India (2014)CrossRef

21.

Bruns, A., Highfield, T.: Political networks on Twitter: tweeting the Queensland state election. Inf. Commun. Soc. 16(5), 667–691 (2013)CrossRef

22.

Xiang, Z., Gretzel, U.: Role of social media in online travel information search. Tourism Manag. 31(2), 179–188 (2010)CrossRef

23.

Holsti, O.R.: Content Analysis for the Social Sciences and Humanities, pp. 602–611. Addison-Wesley, Reading (1969)

24.

Riff, D., Lacy, S., Fico, F.: Analyzing Media Messages: Using Quantitative Content Analysis in Research. Routledge, New York (2014)

25.

Chau, M., Jennifer, X.: Business intelligence in blogs: understanding consumer interactions and communities. MIS Q. 36(4), 1189–1216 (2012)

26.

Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRef

27.

Burt, R.S., Kilduff, M., Tasselli, S.: Social network analysis: foundations and frontiers on advantage. Ann. Rev. Psychol. 64, 527–547 (2013)CrossRef

28.

Scott, P.J., Wasserman, S. (eds.): Models and Methods in Social Network Analysis, vol. 28. Cambridge University Press, Cambridge (2005)

29.

Schmidt, R., Lyytinen, K., Keil, P.C.M.: Identifying software project risks: an international Delphi study. J. Manag. Inf. Syst. 17(4), 5–36 (2001)

30.

Osborne, J., et al.: What ideas‐about‐science should be taught in school science? A Delphi study of the expert community. J. Res. Sci. Teach. 40(7), 692–720 (2003)CrossRef

31.

Gokhale, A.A.: Offshore outsourcing: a Delphi study. J. Inf. Technol. Case Appl. Res. 9(2), 6–18 (2007)MathSciNet

32.

Dyer, L., Blancero, D.: Workplace 2000: A Delphi-Study (1992)

33.

Wang, X., et al.: Topic sentiment analysis in Twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM (2011)

34.

Small, T.A.: What the hashtag? A content analysis of Canadian politics on Twitter. Inf. Commun. Soc. 14(6), 872–895 (2011)CrossRef

35.

Lau, J.H., Collier, N., Baldwin, T.: On-line trend analysis with topic models: \#Twitter trends detection topic model online. In: COLING (2012)

36.

Hanna, R., Rohm, A., Crittenden, V.L.: We’re all connected: the power of the social media ecosystem. Bus. Horiz. 54(3), 265–273 (2011)CrossRef

37.

Jiang, B., Claramunt, C.: Topological analysis of urban street networks. Environ. Plann. B: Plann. Des. 31(1), 151–162 (2004)CrossRef

38.

Kwak, H., et al.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web. ACM (2010)

39.

Java, Akshay, et al.: Why we Twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. ACM (2007)

40.

Huberman, B.A., Romero, D.M., Wu, F.: Social networks that matter: Twitter under the microscope (2008)

41.

Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)

Titel: Insights from Twitter Analytics: Modeling Social Media Personality Dimensions and Impact of Breakthrough Events
verfasst von: Akshat Lakhiwal
Arpan Kumar Kar
Verlag: Springer International Publishing
Buch: Social Media: The Good, the Bad, and the Ugly
Print ISBN: 978-3-319-45233-3

Electronic ISBN: 978-3-319-45234-0

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-45234-0_47

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner