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Modeling blogger influence in a community

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Abstract

Blogging has become a popular and convenient way to communicate, publish information, share preferences, voice opinions, provide suggestions, report news, and form virtual communities in the Blogosphere. The blogosphere obeys a power law distribution with very few blogs being extremely influential and a huge number of blogs being largely unknown. Regardless of a (multi-author) blog being influential or not, there are influential bloggers. However, the sheer number of such blogs makes it extremely challenging to study each one of them. One way to analyze these blogs is to find influential bloggers and consider them as the community representatives. Influential bloggers can impact fellow bloggers in various ways. In this paper, we study the problem of identifying influential bloggers. We define influential bloggers, investigate their characteristics, discuss the challenges with identification, develop a model to quantify their influence, and pave the way for further research leading to more sophisticated models that enable categorization of various types of influential bloggers. To highlight these issues, we conduct experiments using data from blogs, evaluate multiple facets of the problem, and present a unique and objective evaluation strategy given the subjectivity in defining the influence, in addition to various other analytical capabilities. We conclude with interesting findings and future work.

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Notes

  1. http://weblogs.macromedia.com/.

  2. http://www.sifry.com/alerts/archives/000436.html.

  3. http://www.blogpulse.com.

  4. More details on identifying and measuring these indicators are provided in Sect. 3.

  5. Note that K is a user specified parameter.

  6. http://www.blogpulse.com.

  7. http://royal.pingdom.com/2011/01/12/internet-2010-in-numbers/.

  8. A reason we did not adopt any of these is their computation is beyond the scope of this work. We use some simpler measure to examine its effect in determining influence.

  9. http://technorati.com/developers/api/cosmos.html.

  10. http://www.nielsenbuzzmetrics.com/cgm.asp.

  11. http://www.tuaw.com/.

  12. http://technorati.com/developers/api/cosmos.html.

  13. TUAW was setup in February 2004.

  14. This dataset will be made available upon request for research purposes.

  15. http://www.tuaw.com/2007/01/09/iphone-will-not-allow-user-installable-applications/.

  16. http://www.tuaw.com/2007/01/09/macworld-2007-keynote-liveblog/.

  17. http://www.maczot.com/.

  18. http://www.tuaw.com/2007/01/04/xpad-developer-says-maczot-and-brian-ball-ripped-him-off/.

  19. http://www.digg.com/.

  20. We get this data using Digg API.

  21. On average, 70–80 blog posts from TUAW are submitted to Digg every month, so we pick 20 most “digged” or influential posts to avoid under-sampling or over-sampling.

  22. In early stage of the blog site, there are a few cases in which there was little blogging activity such as Feb-04, Oct-04, and Nov-04, resulting in fewer than five influentials.

  23. http://www.engadget.com.

  24. http://blogtrackers.fulton.asu.edu/.

  25. http://kdl.cs.umass.edu/data/dblp/dblp-info.html.

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Acknowledgments

This research was funded in part by the National Science Foundations Social-Computational Systems (SoCS) Program within the Directorate for Computer and Information Science and Engineerings Division of Information and Intelligent Systems (Award numbers: IIS-1110868 and IIS-1110649), the US Office of Naval Research (Grant number: N000141010091), and the US Air Force Office of Scientific Research (Grant number: FA95500810132). We gratefully acknowledge this support.

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Authors and Affiliations

  1. University of Arkansas at Little Rock, Little Rock, AR, USA

    Nitin Agarwal

  2. Arizona State University, Tempe, AZ, USA

    Huan Liu

  3. Yahoo! Labs, Santa Clara, CA, USA

    Lei Tang

  4. University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

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  1. Nitin Agarwal
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  2. Huan Liu
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Correspondence to Nitin Agarwal.

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Agarwal, N., Liu, H., Tang, L. et al. Modeling blogger influence in a community. Soc. Netw. Anal. Min. 2, 139–162 (2012). https://doi.org/10.1007/s13278-011-0039-3

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