Mapping the Australian political blogosphere
ABSTRACT Network crawling and visualisation tools and other datamining systems are now advanced enough to provide
significant new impulses to the study of cultural activity on the Web. A growing range of studies focus on
communicative processes in the blogosphere – including for example Adamic & Glance’s 2005 map of political
allegiances during the 2004 U.S. presidential election and Kelly & Etling’s 2008 study of blogging practices in
There remain a number of significant shortcomings in the application of such tools and methodologies to the
study of blogging; these relate both to how the content of blogs is analysed, and to how the network maps
resulting from such studies are understood. Our project highlights and addresses such shortcomings.
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ABSTRACT: This paper undertakes an overview of two developments in online media that coincided with the 'year-long campaign' that was the 2007 Australian Federal election. It discusses the relatively successful use of the Internet and social media in the 'Kevin07' Australian Labor Party campaign, and contrasts this to the Liberal-National Party's faltering use of You Tube for policy announcements. It also notes the struggle for authority in interpreting polling data between the mainstream media and various online commentators, and the 'July 12 incident' at The Australian, where it engaged in strong denunciation of alleged biases and prejudices among bloggers and on political Web sites. It concludes with consideration of some wider implication for political communication and the politics-media relationship, and whether we are seeing trends towards dispersal and diversification characterising the 'third age' of political communication.
Mapping the Australian Political Blogosphere
Dr Axel Bruns, Creative Industries Faculty, Queensland University of Technology
Brisbane, Australia – email@example.com – http://snurb.info/ – http://gatewatching.org/
Lars Kirchhoff, Thomas Nicolai, Sociomantic Labs
Berlin, Germany – lars.kirchhoff, firstname.lastname@example.org – http://sociomantic.com/
The blogosphere allows for the networked, decentralised, distributed discussion and deliberation on a
wide range of topics. Based on their authors’ interests, only a subset of all blogs will participate in any
one topical debate. Even within such debates, there will be an uneven distribution of participation based
on a variety of sociocultural factors:
the time available for any individual blogger to participate,
the blogger’s level of interest in the topic,
the blogger’s awareness of other blogs discussing the topic (which they link and respond to),
the blogger’s status amongst their peers (which may determine how aware others are of the
blog, and thus whether they will read, comment on, link to, or respond to the blogger’s posts),
the quality of the blogger’s writing and contributions,
the blogger’s specific interests in the topic (which may lead them to focus on particular aspects
of the wider topic),
and additional factors including the blogger’s political ideology, gender, age, location,
sociodemographic status (to the extent that these are evident from the blog), as well as the
language they write in.
In combination, these factors mean that networked debate on specific topics in the blogosphere is
characterised by clustering (Barabási, Albert & Jeong, 1999; Newman, Watts & Strogatz, 2002; Watts,
1999). For any one topic, there are likely to be one or multiple clusters of highly active and closely
interlinked blogs, surrounded by a looser network of blogs which are less active contributors to the
debate and are less densely linked to it. Individual clusters in the topical debate may be able to be
distinguished according to certain factors: for example, their topical specialisation (focussing on specific
sub‐topics of the wider debate) or their shared identity (e.g. a common national, ethnic, or ideological
Such blog‐based debate is difficult to conceptualise under the general terms of the Habermasian
public sphere model (which as formulated depends on the existence of a dominant mass media to
ensure that all citizens are able to be addressed by it; see Habermas 2006); at a smaller level, however,
it may be possible to understand networked discussion on specific topics in the blogosphere to
constitute what may be described as a public spherule (Bruns, 2008). Rather than seeing networked
political debate in terms of the operations of a public sphere, we can think about a group of topical
discussion clusters of sufficient size and interconnection providing a substitute for their participants. It
may be that when layered on top of one another, the public spherules on various topics of public
interest can stand in as a replacement for the conventional public sphere (whose existence is
undermined by the decline of the mass media as mass media; see (Castells, 2007). This networked public
sphere would necessarily be more decentralised than the conventional, Habermasian model of the
Our project aims to develop a rigorous and sound methodology for the study of this networked
2. Research Framework
While qualitative evidence for the networked patterns of discussion, debate, and deliberation in the
blogosphere is readily available, it is more difficult to establish a solid quantitative picture of blog‐based
topical discussion networks and their cluster patterns. Large numbers of blogs (and individual blog
posts, links, and comments) are likely to be involved in a quantitative study of blog‐based discussion
patterns. Hence, automated data collection and analysis is necessary. Any tools used for this purpose
need to be able to distinguish between the different units of analysis: in terms of content, the blog posts
themselves, blog comments, blogrolls, and ancillary (static) content; in terms of links, topical links in
blog posts, commenter‐provided links, blogroll links, and generic links elsewhere on the site.
Distinctions between these different categories build on the following assumptions:
The core underlying assumption is that the vast majority of bloggers write about topics which
interest them (rather than claiming an interest they don’t have). This should not be understood
to claim that bloggers cover all the topics they are interested in, however – the topics covered
on any one blog constitute merely that subset of all interests which a blogger has deemed it
acceptable to reveal publicly to a general readership. On this basis, we assume that:
a. The complete collection of all blog posts for a given blog provides a reliable indication of
the interests of the individual blogger (as expressed publicly); the development of these
interests may be further traced by tracking changes in topical coverage over time.
b. A comparison of bloggers’ interests (in total, for specific periods of time, and/or in relation
to broad topical domains) across multiple blogs indicates the distribution of topical interest
across the blogosphere (at least for the subset of the entire blogosphere included in the
c. A comparison between the blogger’s postings on specific topics, and the collection of
reader comments to these postings, indicates the level of agreement or disagreement
between blogger and commenters (at least for blogs with substantial commenting activity).
The core underlying assumption is that links to other Websites indicate a recognition of the
linked content as ‘interesting’ (for a variety of possible reasons, and potentially indicating
approval or disapproval). By extension, this also confers a certain amount of reputation and
attention on the creator of the linked content (again,this accrued reputation can be either
positive or negative).
We also assume that linking patterns predict traffic and influence. The more incoming links any
piece of content has, the more likely visitors are to see it, and this increases its potential to
influence readers. Further, the outgoing links of sites which themselves receive many incoming
links are more powerful in directing traffic and conferring influence than the outgoing links of
little‐known sites. Google’s PageRank and Technorati’s authority ranking operate on similar
On this basis, we assume that patterns of interlinkage indicate the existence of a network of
attention. These patterns are indicators of visibility and influence. In these patterns, the balance
of incoming and outgoing links for any one site or page warrants special attention. Specifically,
a. Patterns of interlinkage between contemporaneous blogrolls indicate the existence of a
long‐term network of recognition between peers. Sites with many incoming and outgoing
links may be understood as hubs for communication in this network; sites with many
incoming, but limited outgoing links may be understood as central sources for information;
sites with many outgoing but few incoming links may be understood as (not necessarily
central) distributors of attention to other members of the network. The gradual evolution
of such networks can be traced over time.
b. Patterns of interlinkage between contemporaneous blog posts (and other post‐level
content) indicate the existence of a network of debate on specific topics. Such networks of
debate can be seen to persist over greater or lesser periods of time. Posts with many
incoming links may be understood as making an important (possibly controversial) original
contribution to the debate; posts with many incoming and outgoing links may be
understood as making an important discursive contribution to the debate; posts with many
outgoing links may be understood as introductions to or summaries of ongoing debate.
c. Aggregated from the level of the blog post to that of the blog, these patterns of
interlinkage also indicate the role of the overall blogs in topical debate networks. Blogs
with many incoming and outgoing links may be understood as central hubs for
communication on this topic; blogs with many incoming, but limited outgoing links may be
understood as central sources for information on the topic; blogs with many outgoing but
few incoming links may be understood as (not necessarily central) distributors of attention
to other members of the network. A comparison of these short‐term debate networks over
time and across topics indicates the fluctuation of centrality; sites whose centrality remains
high over time can be seen as having significant authority overall, while sites whose
centrality is high only for specific topics can be seen as having significant authority only for
d. Patterns of interlinkage between blog posts and comments indicate that posts or
comments have an ongoing relevance to particular networked debates. If a comment is
linked to in a further post (either on the blog on which the comment was posted, or
elsewhere), it indicates that the comment has itself provoked further discussion and
commentary, and that the conversation constitutes a dialogue between blogosphere
authors and commenters. If blog posts are referred to in comments threads, especially if
these are on other blogs, it indicates that the initial post has relevance and influence in an
ongoing, networked debate.
e. Patterns of linkage between current and archived posts on the same blog indicate the blog
author’s continuing interest in and coverage of relevant topics.
3. Research Methodology
In order to conduct a quantitative analysis of blog‐based discussion networks at a content and link level,
a number of tools must be used. Each introduces a number of necessary limitations to the breadth and
depth of study possible. The three key elements of the research process are data gathering and
processing, content analysis, and link network analysis (however, this does not imply that content
analysis necessarily precedes network analysis, or vice versa). Subsequently, it is also possible to extract
and identify common patterns and interrelations between content und network analyses. Additional
work beyond these initial stages could extend into social network analysis, to identify social networks
within the Blogosphere.
Data Gathering and Processing
Blog content of interest to this project is openly available on the Web (content on blogs behind
intranet firewalls and password protected blogs cannot be regarded as being part of public discussion as
we define it here). Further, most blogs offer RSS feeds which alert subscribers to new posts. RSS feeds in
themselves are an insufficient data source, however: some contain only excerpts from whole posts, and
many do not contain links, images, or other functional elements of the blog posts. None contain
comments (though separate RSS feeds for comments to a specific blog post may also be available).
For a full and reliable analysis, it is therefore necessary to scrape entire blog pages with all textual
and functional elements. This, however, also creates problems as it will include the site’s navigational
elements, blogrolls, comments, ads, and other ancillary material in the data gathered. A direct blog
post‐level analysis of the data will therefore produce skewed results.
This means that scraped blog pages must be further processed in order to separate the salient
content (the blog posts itself) from ancillary material; in the process, other salient elements (blogrolls,
comments) can also be gathered and stored in separate categories. Such processing is non‐trivial and
time‐consuming. Further, page layout and formatting is inconsistent across blogs, and the scraped data
processor must be trained for each category or sometimes for individual blogs (for example, although
Wordpress and Blogger software are commonly used, there are many different versions and templates
For practical reasons, and unless direct access to the up‐to‐date page archives of a commercial
search engine is available, the number of blogs scraped will also need to be limited; it is not feasible to
scrape the entire blogosphere, or even a large part of it. Instead, our methodology must content itself
with focussing on a specific and manageable part of the blogosphere – for example, Australian political
blogs. Even here, a comprehensive coverage may be impossible. It is possible that Australian political
blogs exist which are so little‐known and unconnected that they are invisible in standard sources like
Google and Technorati. And generally non‐political or non‐Australian blogs may contain a very
occasional post about Australian politics, but fall outside the scope of the study. Nevertheless, coverage
of a large part of Australia’s political blogosphere is possible, with the core rather than the far periphery
of the network is the focal point of analysis. Even here, though, the list of blogs (and related sites) to be
scraped should be viewed as open and growing, and to be established over multiple iterations of the
Content analysis builds on the data gathered in the scraping process, operating on the level of blog
posts (or blog posts plus blog comments). It uses automated large‐scale quantitative content analysis
tools such as Leximancer (2008) to identify terms, themes, and concepts in the data (or in subsets of the
entire corpus of data), and their interrelationships. Such automated content analysis should be further
followed up by reading selective posts and comments in a more qualitative examination of specific
issues, concepts or conversations.
Potential approaches to content analysis include:
a. Determination of overall key terms, themes, and concepts across the entire corpus.
b. Change of themes over time.
c. Identification of key themes for individual bloggers or groups of blogs.
d. Comparison of commenters’ and bloggers’ content.
e. Comparisons of treatment of key issues between particular blogs and blog communities, or
between clusters of blogs.
Network analysis focusses on the network of interlinkages between blogs at blogroll, blog post, and
blog comment levels. It uses automated large‐scale network analysis tools such as VOSON (2008) to
trace the networks of interlinkage and identify clusters of closely interlinked nodes in the network,
distinguishing also between inlinks and outlinks.
Potential approaches to link network analysis include:
a. Identification of static networks of blogs using blogroll links.
b. Identification of discursive networks on specific issues using blog post links.
c. Identification of discursive networks on specific topics above the level of blog posts.
d. Identification of general and specific discussion leaders.
There are many opportunities for correlations between conceptual and network analyses (and for
further triangulation using additional sources, including closely reading posts and threads, comparison
with information about key themes in the mainstream media during specific timeframes, and correlation
with site rank indicators such as Google’s PageRank or Technorati’s authority index). Indeed, neither
content nor network analyses in isolation provide a detailed picture of the blogosphere; there is a need
to augment one with the other and with other data.
Possible combination analyses include:
a. Relating network fluctuations to changing topical focus.
b. Correlating network and concept clusters.
c. Identifying distinguishing features of core blogs.
d. Correlation with external measures of site rank.
Further opportunities for combined analyses may be identified during the course of our research.
Generally, all analysis models outlined above may be deepened through close readings of blogs, in
addition to the automated methods on which this methodology builds.
A number of limitations apply for this research programme and have been already identified above:
For practical reasons, analysis is necessarily limited to a subset of the overall blogosphere. It
may miss aspects of the data which exceed the limits of the group of blogs studied here. Thus,
the quality of findings from analysis is likely to be better nearer the core of the concept and link
networks than it is on the periphery.
For some of the analytical approaches outlined above, additional limitations may need to be
introduced (e.g. selecting a specific timeframe or a specific cluster of blogs for in‐depth study).
Such limitations suffer from similar border issues, and repeated analysis with differently
defined limitations may need to be performed to compare outcomes and optimise the
The identification of concept and network clusters makes certain assumptions about what
constitutes a cluster (that is, to what degree the correlation of terms and the interlinkage of
sites are indicative of close clustering). Experimentation with cluster definitions and with
various measures of proximity may be necessary to compare outcomes and optimise the
5. Applications of this Research
The research methodology described enables researchers to
indicate the shape of the networked public sphere overall, and of the individual public
spherules which we assume may constitute it;
show the level of polarisation of or interconnection between the participants in public debate
within any such public spherule;
indicate similarities and differences between various subsets of the overall blogosphere, as
defined for example by topic, nationality, or, language;
track the evolution and dissemination of individual memes (terms, themes, concepts) across
the blogosphere, thereby providing a quantitative basis for the application of extant
communications theories to communication in the blogosphere;
show evidence of the collective knowledge distributed across the blog network.
Our early work on this project demonstrates this research approach in practice, and showcases
early findings from an exploratory study of the Australian political blogosphere.
Barabási, A.‐L., Albert, R., & Jeong, H. 1999. “Scale‐Free Characteristics of Random Networks: The
Topology of the World‐Wide Web.” In Physica A 281, pp. 69‐77.
Bruns, A. 2008. “Life beyond the Public Sphere: Towards a Networked Model for Political Deliberation.”
In Information Polity 13 (1‐2), pp. 65‐79.
———. 2007. “Methodologies for Mapping the Political Blogosphere: An Exploration Using the
IssueCrawler Research Tool.” In First Monday 12 (5). http://www.uic.edu/htbin/cgiwrap/bin
/ojs/index.php/fm/article/view/1834/1718 (accessed 29 Apr. 2008).
Castells, M. 2007. “Communication, Power and Counter‐Power in the Network Society.” In International
Journal of Communication 1, pp. 238‐266.
Habermas, J. 2006. “Political Communication in Media Society: Does Democracy Still Enjoy an Epistemic
Dimension?” In Communication Theory 16 (4), pp. 411‐26.
Leximancer. 2008. http://www.leximancer.com/ (accessed 29 Apr. 2008).
Newman, M. E. J., Watts, D. J., & Strogatz, S. 2002. “Random Graph Models of Social Networks.” In PNAS
99 (1), pp. 2566‐2572.
VOSON: Virtual Observatory for the Study of Online Networks. 2008. http://voson.anu.edu.au/
(accessed 29 Apr. 2008).
Watts, D. J. 1999. “Networks, Dynamics, and the Small‐World Phenomenon.” In The American Journal of
Sociology 105 (2), 493‐527.