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International Handbook of Internet Research pp 233–249Cite as

Web Content Analysis: Expanding the Paradigm

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Abstract

Are established methods of content analysis (CA) adequate to analyze web content, or should new methods be devised to address new technological developments? This article addresses this question by contrasting narrow and broad interpretations of the concept of web content analysis. The utility of a broad interpretation that subsumes the narrow one is then illustrated with reference to research on weblogs (blogs), a popular web format in which features of HTML documents and interactive computer-mediated communication converge. The article concludes by proposing an expanded Web Content Analysis (WebCA) paradigm in which insights from paradigms such as discourse analysis and social network analysis are operationalized and implemented within a general content analytic framework.

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Notes

  1. 1.

    While McMillan (2000) acknowledges that “the size of the sample depends on factors such as the goals of the study” (p. 2, emphasis added), she does not mention that different research goals/questions might call for different types of samples. Rather, she asserts that random samples are required for “rigor” in all CA studies—a claim that many researchers would dispute (see, e.g., note 5).

  2. 2.

    For descriptions of these and other classic interrater reliability measures, see Scott (1955), Holsti (1969), and Krippendorff (1980, 2008).

  3. 3.

    In a review of 25 years of content analyses, Riffe and Freitag (1997; cited in Weare & Lin, 2000) found that most studies were based on convenience or purposive samples; only 22.2% of the studies attempted to be representative of the population of interest.

  4. 4.

    On grounded theory, see Glaser and Strauss (1967).

  5. 5.

    Herring (2004, p. 350) notes that “in CMDA, [sampling] is rarely done randomly, since random sampling sacrifices context, and context is important in interpreting discourse analysis results.”

  6. 6.

    This estimate is based on a report that the number of blogs created at major hosts was 134-144 million in October 2005 (http://www.blogherald.com/2005/10/10/the-blog-herald-blog-count-october-2005/, accessed December 7, 2007). Blog creation, especially in countries outside the U.S., has increased since then, although many blogs have also been abandoned (Wikipedia, June 28, 2008).

  7. 7.

    The (We)blog Research on Genre (BROG) project. See http://en.wikipedia.org/wiki/BROG, accessed August 26, 2009.

  8. 8.

    For example, Herring, Scheidt, et al. (2004, 2005) found that contrary to popular claims that blog entries typically contain links and link often to other blogs, the average number of links in entries in randomly-selected blogs was .65, and most entries contained 0 links. Moreover, the majority of links were to websites created by others, with links to other blogs coming in a distant third.

  9. 9.

    See, e.g., Herring, Scheidt, et al. (2004, 2005); Mishne and Glance (2006).

  10. 10.

    This study is an exception to the generalization that most computational web studies do not orient toward content analysis. The stated goal of Nakajima et al. (2005, p. 1) is to capture and analyze “conversational web content” in blogs.

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Authors and Affiliations

  1. School of Library and Information Science, Bloomington, Indiana, USA

    Susan C. Herring

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  1. Susan C. Herring
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Correspondence to Susan C. Herring .

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  1. Dept. Political Science, Virginia Tech, Major Williams Hall 531, Blacksburg, 24061, USA

    Jeremy Hunsinger

  2. Peachtree St. NE., 620, Atlanta, 30308, USA

    Lisbeth Klastrup

  3. Fac. Media, Society & Culture, Curtin University, Kent St., Bentley, 6102, Australia

    Matthew Allen

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Herring, S.C. (2009). Web Content Analysis: Expanding the Paradigm. In: Hunsinger, J., Klastrup, L., Allen, M. (eds) International Handbook of Internet Research. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9789-8_14

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