Skip to main content
Log in

Who blogs what: understanding the publishing behavior of bloggers

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Are bloggers’ topical coverages related to their contributions, impacts, and publishing styles in the blogosphere? We investigated this question by grouping bloggers on the basis of their topical coverages and comparing their publishing behaviors. From a blog website with more than 370,000 posts, we first identified two types of bloggers: specialists and generalists. Then we studied and compared their respective publishing behaviors in the blogosphere. Our analysis suggested that bloggers with different topical coverages do behave in different ways. Specialists generally make more contributions than generalists. Specialists also tend to publish more on weekdays, during business hours, and on a more regular basis. We also revealed that specialists also have different publishing behaviors, with only a small fraction creating a large “buzz” or producing a voluminous output. As blogs start to gain more business value, an extensive analysis like ours can help various stakeholders in the blogosphere maximize their share of the value chain.

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.

Similar content being viewed by others

References

  1. Adamic, L.A., Glance, N.: The political blogosphere and the 2004 U.S. election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, pp. 36–43. ACM (2005)

  2. Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the influential bloggers in a community. In: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 207–218. ACM (2008)

  3. Agarwal, D., Gabrilovich, E., Hall, R., Josifovski, V., Khanna, R.: Translating relevance scores to probabilities for contextual advertising. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, pp. 1899–1902. ACM (2009)

  4. Alexa.com: Top sites. http://www.alexa.com/topsites (2011). Accessed 18 March 2011

  5. Anagnostopoulos, A., Broder, A.Z., Gabrilovich, E., Josifovski, V., Riedel, L.: Just-in-time contextual advertising. In: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management, pp. 331–340. ACM (2007)

  6. Bansal, N., Chiang, F., Koudas, N., Tompa, F.W.: Seeking stable clusters in the blogosphere. In: Proceedings of the 33rd International Conference on Very Large Databases, pp. 806–817. VLDB Endowment (2007)

  7. Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  8. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  9. BlogPulse.com: Blogpulse stats. www.blogpulse.com (2011). Accessed 9 March 2011

  10. Brooks, C.H., Montanez, N.: Improved annotation of the blogosphere via autotagging and hierarchical clustering. In: Proceedings of the 15th International Conference on World Wide Web, pp. 625–632. ACM (2006)

  11. Chesley, P., Vincent, B., Xu, L., Srihari, R.: Using verbs and adjectives to automatically classify blog sentiment. In: AAAI symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pp. 27–29 (2006)

  12. Chi, Y., Tseng, B.L., Tatemura, J.: Eigen-trend: trend analysis in the blogosphere based on singular value decompositions. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM), pp. 68–77. ACM (2006)

  13. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(2), 224–227 (1979)

    Article  Google Scholar 

  14. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. Ser. B (Methodological) 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  15. Domingos, P.: Mining social networks for viral marketing. IEEE Intell. Syst. 20(1), 80–82 (2005)

    Article  MathSciNet  Google Scholar 

  16. Furukawa, T., Ishizuka, M., Matsuo, Y., Ohmukai, I., Uchiyama, K.: Analyzing reading behavior by blog mining. In: Proceedings of the 22nd National Conference on Artificial Intelligence, vol. 2, pp. 1353–1358. AAAI Press (2007)

  17. Geyer, W., Dugan, C.: Inspired by the audience: a topic suggestion system for blog writers and readers. In: Proceedings of the 2010 ACM conference on Computer Supported Cooperative Work (CSCW), pp. 237–240. ACM (2010)

  18. Glance, N., Hurst, M., Nigam, K., Siegler, M., Stockton, R., Tomokiyo, T.: Deriving marketing intelligence from online discussion. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 419–428. ACM (2005)

  19. Glance, N., Hurst, M., Tomokiyo, T.: Blogpulse: automated trend discovery for weblogs. In: WWW 2004 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics (2004)

  20. Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proceedings of the 13th International Conference on World Wide Web (WWW), pp. 491–501. ACM (2004)

  21. Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. 102(46), 16,569–16,572 (2005)

    Article  Google Scholar 

  22. Kopytoff, V.G.: Aol to acquire techcrunch to bolster its news coverage. http://www.nytimes.com/2010/09/29/technology/29aol.html (2010). Accessed 1 May 2011

  23. Kumar, R., Novak, J., Raghavan, P., Tomkins, A.: Structure and evolution of blogspace. Commun. ACM 47(12), 35–39 (2004). 1035162

    Article  Google Scholar 

  24. Kumar, R., Novak, J., Raghavan, P., Tomkins, A.: On the bursty evolution of blogspace. World Wide Web 8(2), 159–178 (2005)

    Article  Google Scholar 

  25. Lenhart, A., Purcell, K., Smith, A., Zickuhr, K.: Social media and mobile internet use among teens and young adults. Tech. rep., Pew Research Center (2010)

  26. Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., Hurst, M.: Patterns of cascading behavior in large blog graphs. In: SIAM International Conference on Data Mining, pp. 551–556 (2007)

  27. Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proceeding of the 17th International Conference on World Wide Web, pp. 675–684. ACM (2008)

  28. Mishne, G., Rijke, M.d.: Deriving wishlists from blogs: show us your blog, and we’ll tell you what books to buy. In: Proceedings of the 15th International Conference on World Wide Web (WWW), pp. 925–926. ACM, 1135947 (2006)

  29. Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E 67(2), 13 (2003)

    Article  Google Scholar 

  30. Oreilly, T.: What is Web 2.0: Design patterns and business models for the next generation of software. Communications Strategies 65(1st quarter 2007), 17–31 (2007)

    Google Scholar 

  31. Pak, A., Chung, C.W.: A Wikipedia matching approach to contextual advertising. World Wide Web 13(3), 251–274 (2010)

    Article  Google Scholar 

  32. Parameswaran, M., Whinston, A.: Research issues in social computing. J. Assoc. Inf. Syst. 8(6), 336–350 (2007)

    Google Scholar 

  33. Peters, J.W., Kopytoff, V.G.: Betting on news, aol is buying the Huffington post. http://www.nytimes.com/2011/02/07/business/media/07aol.html (2011). Accessed 1 May 2011

  34. Porter, L.V., Sweetser Trammell, K.D., Chung, D., Kim, E.: Blog power: examining the effects of practitioner blog use on power in public relations. Publ. Relat. Rev. 33(1), 92–95 (2007)

    Article  Google Scholar 

  35. Qamra, A., Tseng, B., Chang, E.Y.: Mining blog stories using community-based and temporal clustering. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 58–67. ACM (2006)

  36. Reuters.com: Newsweek and daily beast agree merger. http://www.reuters.com/article/2010/11/12/us-newsweek-dailybeast-idUSTRE6AB0JI20101112 (2010). Accessed 1 May 2011

  37. Schmidt, J.: Blogging practices: an analytical framework. J. Comput-Mediat. Comm. 12(4), 1409–1427 (2007)

    Article  Google Scholar 

  38. Scoble, R., Israel, S.: Naked Conversations: How Blogs are Changing the Way Businesses Talk with Customers. John Wiley and Sons Ltd (2006)

  39. Wallsten, K.: Many sources, one message: political blog links to online videos during the 2008 campaign. J. Polit. Market. 10(1), 88–114 (2011)

    Article  Google Scholar 

  40. White, D.: State of the Blogosphere 2008. Tech. rep., Technorati Inc. (2009)

  41. Wright, J.: Blog Marketing: The Revolutionary New Way to Increase Sales, Build Your Brand, and Get Exceptional Results. McGraw-Hill (2005)

  42. Zhao, K., Ngamassi, L.M., Yen, J., Maitland, C., Tapia, A.: Assortativity patterns in multi-dimensional inter-organizational networks: a case study of the humanitarian relief sector. In: S.K. Chai, J.J. Salerno, P.L. Mabry (eds.) Advances in Social Computing—Proceedings of the 2010 International Conference on Social Computing, Behavioral Modeling, and Prediction (SBP10). LNCS, vol. 6007/2010, pp. 265–272. Springer (2010)

  43. Zhao, K., Qiu, B., Caragea, C., Wu, D., Mitra, P., Yen, J., Greer, G.E., Portier, K.: Identifying leaders in an online cancer survivor community. In: Proceedings of the 21st Annual Workshop on Information Technologies and Systems (WITS’11), pp. 115–120 (2011)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kang Zhao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhao, K., Kumar, A. Who blogs what: understanding the publishing behavior of bloggers. World Wide Web 16, 621–644 (2013). https://doi.org/10.1007/s11280-012-0167-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-012-0167-3

Keywords

Navigation