This document describes a study that used network analysis to analyze audience flow patterns on the World Wide Web. Key findings included that WWW audience flow is highly decentralized, with websites receiving traffic from less than 10 other sites on average. Websites clustered along geo-linguistic lines, with language and geography found to be more influential on audience flows than hyperlink connections. This provides evidence for culturally proximate media consumption over theories of cultural imperialism or one-way global flows. Analyzing a larger sample of websites could help further disentangle the roles of geography and language.

1. Describing Audience Flow on the Internet
Using A Network Analytic Approach
H A R S H TA N E J A
P H D C A N D I DA T E ,
MEDIA, TECHNOLOGY AND SOCIETY
N O R T H W E S T E R N U N I V E R S I T Y,
P R E S E N TA T I O N I N PA N E L : “ H O W
F R A G M E N T E D A R E W E ? PA T T E R N S O F
MEDIA USE AROUND THE GLOBE”
ICA 2012, PHOENIX

2. Aim : To Describe Global WWW Audience Flow
 The World Wide Web (WWW) – Structure and Usage
Patterns
 A Network Analytic Approach to Audience Flow on WWW
 Empirical Test

3. Explaining WWW As a Network of Hyperlinks
Key Findings:
 High centrality of developed nations (Barnett et al, various)
 High centrality of English language websites (Google Research)
Reinforce:
 World Systems Theory (Chase Dunn, 1995)
 Cultural Imperialism & One Way flows (Schiller, 1969; Wildman, 1994)
Hyperlinks do not represent actual audience flows

4. Audience Research on WWW Usage
Key Findings:
 Fragmentation into mass and niche audiences (Anderson, 2006)
 Polarization into red and blue
Alternative Explanations:
 Audiences flow across mass and niche outlets (Elberse, 2008;
Webster and Ksiazek, 2012)
 “Cultural Proximity” drives consumption (Straubhaar,1991)
Need an approach that captures actual flow of audiences

5. Aim : To Describe Global WWW Audience Flow
 The World Wide Web (WWW) – Structure and Usage
Patterns
 A Network Analytic Approach to Audience Flow on WWW
 Empirical Test

6. WWW Audience Flow in Network Analytic Terms
 Media outlets (websites) as “Nodes”
 Ties between nodes based on shared audiences
 Absolute Duplication -% of audiences who access both outlets
(Ksiazek, 2011)
 Achieves undirected network - unable to account for „audience flow‟
 Present Study: Consider „clickstream‟ traffic between
websites to approximate audience flow

7. WWW Audience Flow in Network Analytic Terms
Schematic of a network based on click-stream data using 3 websites

8. Aim : To Describe Global WWW Audience Flow
 The World Wide Web (WWW) – Structure and Usage
Patterns
 A Network Analytic Approach to Audience Flow on WWW
 Empirical Test

9. Data and Method
 Selected top 113 websites based on monthly unique users
from comScore Media Metrix, December 2010
 Constructed a network using “incoming clickstream
traffic” between each pair of websites
 Descriptive network analysis
 Used average clickstream traffic as cut off to define presence or
absence of ties*
 Cluster analysis to segment nodes based on network
positions

10. WWW Audience Flow Highly Decentralized
•Network Centralization of 6% suggesting high clustering and low centralization
•Mean In-Degrees = 9.6, SD 3,Website received traffic from less than 10 websites
Logon
DEVIANTART.COM
SINA.COMSITES
REAL.Com*
METROLYRICS.COM
BLOGGER.COM*
IMAGESHACK.US
IMESH.COM
MICROSOFT.COM*
MSNBC.COM
METACAFE.COM*
DAILYMOTION.COM
TARINGA.NET
MAIL.RU
WIKIA.COM*
DICTIONARY.COM
BABYLON.COM
NAVER.COM
BBC
ESPN
VKONTAKTE.RU
MYWEBSEARCH.COM
MINICLIP.COM
AVG.COM
GUARDIAN.CO.UK
HP.COM
PAYPAL.COM
EHOW
ALIBABA.COM
ASK.COM
FACEBOOK.COM
ADOBE.COM
PHOTOBUCKET.COM
4SHARED.COM
YANDEX
ORKUT.COM.BR
EBAY.DE*
GOO.NE.JP
FILESTUBE.COM
EBAY.COM*
AMAZON
HUFFINGTONPOST.COM
MOZILLA.COM
RAPIDSHARE.COM
SOSO.COM
HUBPAGES.COM
LIVEJOURNAL.COM*
BESTBUY.COM
163.COM
QQ.COM*
GOOGLE
HOTFILE.COM
360.CN
SOGOU.COM
DEPOSITFILES.COM
XUNLEI.COM
BAIDU.COM
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11. WWW Flows Cluster on Geo-Linguistic Lines
Region
Global
USA
China
Japan
Korea
Brazil
Russia

12. Central (Global) Cluster has Websites with
Multiple Language and Geographic Versions

13. Examples of Geo-Linguistic Clusters
Chinese Cluster Japanese Cluster
Language and geography hard to isolate from
one another as drivers behind clustering

14. Conclusions
 Cultural factors such as language and geography seem more
powerful than hyperlinks in describing global WWW
audience flow
 More evidence of culturally proximate consumption than
evidence of cultural imperialism or one way flows
 Little evidence of centrality of English language or core countries
 Inclusion of larger samples of websites shall help
disentangle roles of geography and language

15. Questions and Comments
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