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Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
Sunbelt05
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Sunbelt05
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Sunbelt05

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    • 1. Social Network Dynamics in the Blogosphere The Blog Research on Genre (BROG) Project School of Library and Information Science Indiana University Bloomington
    • 2. BROG project members <ul><li>Susan Herring </li></ul><ul><li>Inna Kouper </li></ul><ul><li>Sarah Mercure </li></ul><ul><li>John Paolillo </li></ul><ul><li>Lois Ann Scheidt </li></ul><ul><li>Peter Welsch </li></ul><ul><li>Elijah Wright </li></ul>
    • 3. The Blogosphere <ul><li>The collective term encompassing all weblogs </li></ul><ul><ul><li>(cf. blog biosphere or ecosystem) </li></ul></ul><ul><li>The “intellectual cyberspace” inhabited by bloggers </li></ul><ul><ul><li>(Wm. Quick, 2001) </li></ul></ul><ul><li>“ Blogs as a community; blogs as a social network” </li></ul><ul><ul><li>( www. samizdata .net ) </li></ul></ul>
    • 4. Previous research <ul><li>One-third of blogs have no hyperlinks </li></ul><ul><li>Small part of the blogosphere is densely interlinked </li></ul><ul><li>‘ A-list’ blogs are central in network </li></ul><ul><li>Cliques exist </li></ul><ul><li>‘ Conversation’ between blogs is sporadic over time </li></ul><ul><li>(Efimova & de Moor, 2005; Herring et al., 2004, 2005; </li></ul><ul><li>Kumar et al., 2003) </li></ul><ul><li>BUT: </li></ul><ul><li>No previous research on change over time </li></ul><ul><li>in blog networks </li></ul>
    • 5. Research question <ul><li>How do networks of links among blogs change over time? </li></ul><ul><ul><li>How quickly? </li></ul></ul><ul><ul><li>To what extent? </li></ul></ul><ul><ul><li>In what ways? </li></ul></ul>
    • 6. Sampling method <ul><li>Random sample of 4 blogs followed by snowball sample out 3 levels from random blogs </li></ul><ul><li>3 samples at 4-month intervals </li></ul><ul><ul><li>April, August, December 2004 </li></ul></ul><ul><ul><li>samples 2 and 3 automated </li></ul></ul><ul><ul><li>5387, 4900, 4367 unique URLS per sample </li></ul></ul><ul><ul><li>(~10,000 total unique URLs) </li></ul></ul>
    • 7. Source blogs <ul><li>pencilinyourhand . blogspot .com </li></ul><ul><li>www. danm .us/ blog </li></ul><ul><li>www. mysocalledblog .com </li></ul><ul><li>orangetang .org/ erica / blogger .html </li></ul>
    • 8. Analytical methods <ul><li>Content analysis </li></ul><ul><ul><li>300 random, 150 core blogs (17+ in-links) </li></ul></ul><ul><ul><ul><li>Themes : current events, politics, religion, technology, etc. </li></ul></ul></ul><ul><ul><ul><li>Blog type : personal journal, filter, k-log, mixed, other </li></ul></ul></ul><ul><ul><ul><li>Gender of blog author </li></ul></ul></ul><ul><li>Results compared for three samples </li></ul>
    • 9. Analytical methods (cont.) <ul><li>Social network analysis (Degenne & Forsé, 1999) </li></ul><ul><ul><li>based on links in sidebars (‘blogrolls’) </li></ul></ul><ul><ul><ul><li>Centrality </li></ul></ul></ul><ul><ul><ul><li>Reciprocity </li></ul></ul></ul><ul><li>Visualization of network core </li></ul><ul><ul><li>blogs with 10+ in-links </li></ul></ul><ul><ul><li>Kamada-Kawai layout in R </li></ul></ul><ul><li>Results compared for three samples </li></ul>
    • 10. Content analysis: Random subsample <ul><li>Themes </li></ul><ul><ul><li>Personal > current events/politics > technology > religion </li></ul></ul><ul><li>Blog type </li></ul><ul><ul><li>Filter - avg. 42%, increasing over time </li></ul></ul><ul><ul><li>Personal journal - avg. 38%, decreasing over time </li></ul></ul><ul><li>Gender of blog author </li></ul><ul><ul><li>Male - avg. 65%, increasing over time </li></ul></ul><ul><li>Cf. Herring et al. (2004) </li></ul><ul><ul><li>70% of blogs are personal journals;13% are filters </li></ul></ul><ul><ul><li>50% of blog authors are female </li></ul></ul>
    • 11. Content analysis: Core sample <ul><li>Themes </li></ul><ul><ul><li>Religion > current events/politics > personal > technology </li></ul></ul><ul><li>Blog type </li></ul><ul><ul><li>Filter - avg. 49%, increasing over time </li></ul></ul><ul><ul><li>personal journal - avg. 15.6%, decreasing over time </li></ul></ul><ul><li>Gender of blog author </li></ul><ul><ul><li>Male - avg. 66% </li></ul></ul><ul><li>Core: blogs with 17+ in-links </li></ul>
    • 12. Content analysis: Comparison <ul><li>Random subsample </li></ul><ul><ul><li>few in-links (peripheral to network) </li></ul></ul><ul><ul><li>diverse content </li></ul></ul><ul><ul><li>high turn-over of individual blogs </li></ul></ul><ul><ul><ul><li>13% shared across 3 samples </li></ul></ul></ul><ul><li>Core sample </li></ul><ul><ul><li>many in-links </li></ul></ul><ul><ul><li>focused on religion, politics, morality, education </li></ul></ul><ul><ul><li>stable membership over time </li></ul></ul><ul><ul><ul><li>75% shared across 3 samples </li></ul></ul></ul>
    • 13. Social network analysis: Centrality <ul><li>‘ A-list’ blogs are central </li></ul><ul><ul><li>All four source blogs lead to 25/37 A-list blogs </li></ul></ul><ul><ul><li>Avg. 3 degrees of separation from any source blog to any A-list blog (range 1.8 - 4.7 degrees) </li></ul></ul><ul><ul><ul><li>tendency to increase in closeness over time </li></ul></ul></ul><ul><li>Catholic blogs are ‘core of the core’ </li></ul><ul><ul><li>pattern like A-list </li></ul></ul>
    • 14. Social network analysis: Reciprocity <ul><li>A-list blogs attract more links </li></ul><ul><ul><li>Tend to be found in reciprocal relations with other A-list blogs </li></ul></ul><ul><ul><li>Non-A-list blogs link preferentially to A-list blogs, but low rate of reciprocation </li></ul></ul><ul><li>Change over time </li></ul><ul><ul><li>Increase in reciprocal linking of A-list blogs (p = .001) </li></ul></ul><ul><ul><li>Decrease in reciprocal linking of non-A-list blogs (p = .001) </li></ul></ul><ul><li>Catholic blogs pattern like A-list </li></ul>
    • 15. Visualization <ul><li>Cut-off at 10 in-degrees (350 blogs) </li></ul><ul><li>Three thematic clusters emerge: </li></ul><ul><ul><ul><li>Catholicism ( red ) </li></ul></ul></ul><ul><ul><ul><li>Politics/current events ( green ) </li></ul></ul></ul><ul><ul><ul><li>Homeschooling ( blue ) </li></ul></ul></ul><ul><li>Catholic (and some political) blogs consolidate over time </li></ul><ul><li>Other clusters fragment or disperse </li></ul>
    • 16. Sample 1 (April 2004)
    • 17. Sample 2 (August 2004)
    • 18. Sample 3 (December 2004)
    • 19. Animation
    • 20. Study limitations <ul><li>Only four random sources, three of them filter blogs, one Catholic </li></ul><ul><ul><li>Filters more likely to have links (Blood, 2002) </li></ul></ul><ul><ul><li>Catholic blogs more likely to link to each other? </li></ul></ul><ul><li>Snowball sampling creates bias towards connectivity </li></ul><ul><ul><li>Overestimates overall connectivity </li></ul></ul><ul><li>First sample was collected manually, second and third samples via automated crawl </li></ul><ul><ul><li>May not be strictly comparable </li></ul></ul>
    • 21. How does the network change? <ul><li>Core gets tighter </li></ul><ul><ul><li>religious, politically conservative blogs </li></ul></ul><ul><li>Periphery gets looser </li></ul><ul><ul><li>thematically-diverse, albeit disproportionately filter-type, male blogs </li></ul></ul><ul><li>Change is evident at 4-month intervals </li></ul>
    • 22. Possible explanations <ul><li>Political/religious discourses increasingly polarized </li></ul><ul><ul><li>US 2004 presidential campaign </li></ul></ul><ul><li>Tendency for cliques to become more cliquish </li></ul><ul><ul><li>If so, should be demonstrable for other cliques in the blogosphere </li></ul></ul>
    • 23. Future directions <ul><li>Conduct longitudinal network analysis starting from other source blogs, e.g. </li></ul><ul><ul><li>Politically liberal </li></ul></ul><ul><ul><li>Non-filter types </li></ul></ul><ul><ul><li>Female authors </li></ul></ul><ul><li>Sample at shorter intervals </li></ul><ul><li>Track network evolution over long time spans </li></ul>
    • 24. Contact: [email_address]

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