New directions forblog network mapping

IR11Göteborg, Sweden, 22 October 2010

Tim HighfieldQueensland University of Technology

Lars KirchhoffThomas Nicolai

Sociomantic Labs

Overview

Blog network mappingvisualising connections between groups of sites

links, affiliations,friendships, clustersreferences, opposition

for large datasets, enables depictionof non-physical space, make sense of data,(although risk of focussing on 'sciencey'visualisations rather than analysis)

Previous examples

Adamic and Glance (2005)U.S. - 2004 Presidential election

RTGI/linkfluence (2007 - present)France – 2007 Presidential electionU.S. - 2008 Presidential electionEurope – 2009 EU elections

Kelly and Etling (2008)Persian-language

Etling et al. (2009)Arabic blogs

Strengths

Overview of large/long-term datasets

Identification of clusters/groups, including around themes or activities

Locate most active sites, popular sources

Weaknesses

Differentiation between types of link, link semantics,link frequency

Temporal variations – event-oriented peaks or troughs, context for blog activity

Research context

Study of French and Australian politicalblogs (and associated sites)

Data collection: January – August 2009

Data used: blog posts and links

Focus on comparing whole period activityand event- based, topical networks

New directions

1. Types of link

Blogrolls vs. in-text citations(vs. in-text in topical network)(vs. comments, other platforms)

Blogrol ls

French political blogsJan-Aug 2009

Size:in-degree

Colour:out-degree

Out-l inksFrench political blogsJan-Aug 2009

Size:in-degree

Colour:out-degree

2. Dynamics / Snapshots

Isolating posts around particular topics/people/events

Day-by-day variations, changes in network over time

Topical references vs. most cited resources overall,topical blog activity compared to total output during period

Out-l inks

Size:in-degree

Colour:out-degree

French political blogs16-25 January 2009

Topical network: Obama inauguration

French political blogs16-25 January 2009

Size:in-degree

Colour:out-degree

3. Mixed methods

Hyperlink analysis + content analysis

locating networks using link data from postsdiscussing particular topic (+ visualisation)

analysing text of posts from individual blogsto identify prominent themes, different approaches to the same topic

4. Geographical representations

Distribution of sites within a link-based networkcompared with geographical distribution of blogs

clustering around major cities?

blogging demographics? (urban/rural?)

Combined with content analysis – different topics beingdiscussed around regions?

5. Beyond the network paradigm?

Hyperlink analysis invites the use of 'networks'...

Other mapping approaches available, depictingprominence of sites (traffic, in-links, rankings),thematic clusters,location, ideologies...

Druaux (2007)Cartographie diablement subjective et approximativede la blogarchie francophone 2007http://www.ouinon.net/index.php?2007/09/24/215-cartograhie-blogosphere-francophone

Munroe (2007)Map of OnlineCommunitieshttp://xkcd.com/256/

Munroe (2010)Updated Map ofOnline Communitieshttp://xkcd.com/802_large/

See also Bloch (2010)http://www.flowtown.com/blog/the-2010-social-networking-map

Lars KirchhoffThomas Nicolai

lars.kirchhoff@sociomantic.comthomas.nicolai@sociomantic.comsociomantic.com @sociomantic

Tim Highfield

t.highfield@qut.edu.autimhighfield.net @timhighfield