Skip to main content

Time- and Event-Driven Modeling of Blogger Influence

  • Reference work entry
  • First Online:
Encyclopedia of Social Network Analysis and Mining

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,500.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adamic L, Glance N (2005) The political blogosphere and the 2004 US election: divided they blog. In: Proceedings of the 3rd international workshop on link discovery, Chicago. ACM, pp 36–43

    Google Scholar 

  • Adar E, Zhang L, Adamic L, Lukose R (2004) Implicit structure and the dynamics of blogspace. In: Workshop on the weblogging ecosystem, vol 13. New York, NY, USA

    Google Scholar 

  • Agarwal N, Liu H, Tang L, Yu P (2008) Identifying the influential bloggers in a community. In: Proceedings of the international conference on web search and web data mining, Palo Alto. ACM, pp 207–218

    Google Scholar 

  • Agarwal N, Lim M, Wigand R (2011) Collective action theory meets the blogosphere: a new methodology. In: Networked digital technologies, Macau, pp 224–239

    Google Scholar 

  • Agarwal N, Lim M, Wigand R (2012) Online collective action and the role of social media in mobilizing opinions: a case study on women's right-to-drive campaigns in Saudi Arabia. In: Reddick CG, Aikins SK (eds) Web 20 technologies and democratic governance. Springer, New York, pp 99–123

    Google Scholar 

  • Akritidis L, Katsaros D, Bozanis P (2011) Identifying the productive and influential bloggers in a community. IEEE Trans Syst Man Cybern Part C: Appl Rev 41(5):759–764

    Google Scholar 

  • Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc Natl Acad Sci 106(51):21544–21549

    Google Scholar 

  • Bansal N, Koudas N (2007) Blogscope: a system for online analysis of high volume text streams. In: Proceedings of the 33rd international conference on very large databases, Vienna. VLDB Endowment, pp 1410–1413

    Google Scholar 

  • Bavelas A (1948) A mathematical model for group structures. Hum Organ 7(3): 16Z–30

    Google Scholar 

  • Bazaarvoice (2012) Social trends report 2012. Technical report, Bazaarvoice, Social Summit 2012

    Google Scholar 

  • Borgatti S, Everett M (2006) A graph-theoretic perspective on centrality. Soc Netw 28(4):466–484

    Google Scholar 

  • Davis R (2009) Typing politics: the role of blogs in American politics. Oxford University Press, New York

    Google Scholar 

  • Ekdale B, Namkoong K, Fung T, Hussain M, Arora M, Perlmutter D (2007) From expression to influence: understanding the change in blogger motivations over the blogspan. AEJMC, Washington, DC

    Google Scholar 

  • Freeman L (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239

    Google Scholar 

  • Gill K (2004) How can we measure the influence of the blogosphere. In: WWW 2004 workshop on the weblogging ecosystem: aggregation, analysis and dynamics, New York

    Google Scholar 

  • Goldenberg J, Libai B, Muller E (2001) Using complex systems analysis to advance marketing theory development: modeling heterogeneity effects on new product growth through stochastic cellular automata. Acad Mark Sci Rev 9(3):1–18

    Google Scholar 

  • Gomez-Rodriguez M, Leskovec J, Krause A (2012) Inferring networks of diffusion and influence. ACM Trans Knowl Discov Data (TKDD) 5(4):21

    Google Scholar 

  • Gordon A, Swanson R (2009) Identifying personal stories in millions of weblog entries. In: Third international conference on weblogs and social media, data challenge workshop, San Jose

    Google Scholar 

  • Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83:1420–1443

    Google Scholar 

  • Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: Proceedings of the 13th international conference on world wide web, New York. ACM, pp 491–501

    Google Scholar 

  • Java A, Kolari P, Finin T, Oates T (2006) Modeling the spread of influence on the blogosphere. In: Proceedings of the 15th international world wide web conference, Edinburgh, 22–26 May 2006

    Google Scholar 

  • Karpf D (2007) Measuring influence in the political blogosphere: who's winning and how can we tell? Politics and technology review, George Washington University: Institute for Politics, Democracy and the Internet, pp 33–41

    Google Scholar 

  • Keller E, Berry J (2003) The influentials: one American in ten tells the other nine how to vote, where to eat, and what to buy. Free Press, New York

    Google Scholar 

  • Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC. ACM, pp 137–146

    Google Scholar 

  • Kleinberg J (1999) Authoritative sources in a hyperlinked environment. J ACM (JACM) 46(5):604–632

    MATH  MathSciNet  Google Scholar 

  • Kumar S, Agarwal N, Lim M, Liu H (2009) Mapping socio-cultural dynamics in Indonesian blogosphere. In: 3rd international conference on computational cultural dynamics, Washington, DC, pp 37–44

    Google Scholar 

  • Kumar S, Zafarani R, Abbasi M, Barbier G, Liu H (2010) Convergence of influential bloggers for topic discovery in the blogosphere. In: Chai S-K, Salerno JJ, Mabry PL (eds) Advances in social computing. Springer, Berlin/New York, pp 406–412

    Google Scholar 

  • Kwon Y, Kim S, Park S, Lim S, Lee J (2009) The information diffusion model in the blog world. In: Proceedings of the 3rd workshop on social network mining and analysis, Paris. ACM, p 4

    Google Scholar 

  • Leskovec J, Krause A, Guestrin C, Faloutsos C, Van-Briesen J, Glance N (2007a) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose. ACM, pp 420–429

    Google Scholar 

  • Leskovec J, McGlohon M, Faloutsos C, Glance N, Hurst M (2007b) Cascading behavior in large blog graphs. arXiv preprint arXiv:07042803

    Google Scholar 

  • Li H, Bhowmick S, Sun A (2009) Blog cascade affinity: analysis and prediction. In: Proceedings of the 18th ACM conference on information and knowledge management, Hong Kong. ACM, pp 1117–1126

    Google Scholar 

  • Li Y, Lai C, Chen C (2011) Discovering influencers for marketing in the blogosphere. Inf Sci 181(23): 5143–5157

    Google Scholar 

  • Lim S, Kim S, Kim S, Park S (2011) Construction of a blog network based on information diffusion. In: Proceedings of the 2011 ACM symposium on applied computing, TaiChung. ACM, pp 937–941

    Google Scholar 

  • Moreno J (1934) Who shall survive? vol 58. Nervous and Mental Disease Publishing Company, Washington, DC

    Google Scholar 

  • Nallapati R, Cohen W (2008) Link-PLSA-LDA: A new unsupervised model for topics and influence of blogs. In: International conference for weblogs and social media, Seattle, 2008. AAAI

    Google Scholar 

  • Onishi H, Manchanda P (2012) Marketing activity, blog-ging and sales. Int J Res Mark 29:221–234

    Google Scholar 

  • Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. Technical report 1999-66, Stanford InfoLab, http://ilpubs.stanford.edu:8090/422/, previous number = SIDL-WP-1999-0120

  • Proctor C, Loomis C (1951) Analysis of sociometric data. Res Methods Soc Relat 2:561–285

    Google Scholar 

  • Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, Edmonton. ACM, pp 61–70

    Google Scholar 

  • Song X, Chi Y, Hino K, Tseng B (2007) Identifying opinion leaders in the blogosphere. In: Proceedings of the sixteenth ACM conference on information and knowledge management, Lisboa. Citeseer, pp 971–974

    Google Scholar 

  • Stefanone M, Jang C (2008) Social exchange online: public conversations in the blogosphere. In: Proceedings of the 41st annual Hawaii international conference on system sciences, Waikoloa. IEEE, pp 148–148

    Google Scholar 

  • Stewart A, Chen L, Paiu R, Nejdl W (2007) Discovering information diffusion paths from blogosphere for online advertising. In: Proceedings of the 1st international workshop on data mining and audience intelligence for advertising, San Jose. ACM, pp 46–54

    Google Scholar 

  • Symphoni IRI Group (2012) Millennial shoppers: tapping into the next growth segment. Technical report, Sym-phoni IRI Group

    Google Scholar 

  • Technorati (2011) State of the blogosphere 2011. http://technorati.com/social-media/article/state-of-the-blogosphere-2011-introduction/

  • Zeleny L (1940) Measurement of social status. Am J Sociol 45:576–582

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Agarwal, N., Mahata, D., Liu, H. (2014). Time- and Event-Driven Modeling of Blogger Influence. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_378

Download citation

Publish with us

Policies and ethics