describing audience flow on the internet
TRANSCRIPT
HARSH TANEJA PHD CANDIDATE,
MEDIA, TECHNOLOGY AND SOCIETY NORTHWESTERN UNIVERSITY,
PRESENTATION IN PANEL: “HOW FRAGMENTED ARE WE ? PATTERNS OF
MEDIA USE AROUND THE GLOBE” ICA 2012, PHOENIX
Describing Audience Flow on the Internet Using A Network Analytic Approach
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
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
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
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
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
WWW Audience Flow in Network Analytic Terms
Schematic of a network based on click-stream data using 3 websites
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
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
WWW Audience Flow Highly Decentralized
BAIDU.COM
XUNLEI.COM
DEPOSITFILES.CO
MSO
GOU.CO
M360.CNHO
TFILE.COM
GOO
GLEQ
Q.CO
M*
163.COM
BESTBUY.COM
LIVEJOURNAL.CO
M*
HUBPAGES.COM
SOSO
.COM
RAPIDSHARE.COM
MO
ZILLA.COM
HUFFINGTONPO
ST.COM
AMAZO
NEBAY.CO
M*
FILESTUBE.COM
GOO
.NE.JPEBAY.DE*O
RKUT.COM
.BRYANDEX4SHARED.CO
MPHO
TOBUCKET.CO
MADO
BE.COM
FACEBOO
K.COM
ASK.COM
ALIBABA.COM
EHOW
PAYPAL.COM
HP.COM
GUARDIAN.CO.UK
AVG.COM
MINICLIP.CO
MM
YWEBSEARCH.CO
MVKO
NTAKTE.RUESPNBBCNAVER.CO
MBABYLO
N.COM
DICTIONARY.CO
MW
IKIA.COM
*M
AIL.RUTARINGA.NETDAILYM
OTIO
N.COM
METACAFE.CO
M*
MSNBC.CO
MM
ICROSO
FT.COM
*IM
ESH.COM
IMAGESHACK.US
BLOGGER.CO
M*
METRO
LYRICS.COM
REAL.Com*
SINA.COM
SITESDEVIANTART.CO
MLogon
02468
1012141618
•Mean In-Degrees = 9.6, SD 3,Website received traffic from less than 10 websites•Network Centralization of 6% suggesting high clustering and low centralization
WWW Flows Cluster on Geo-Linguistic Lines
Region
Global
USA
China
Japan
Korea
Brazil
Russia
Central (Global) Cluster has Websites with Multiple Language and Geographic Versions
Chinese Cluster Japanese Cluster
Examples of Geo-Linguistic Clusters
Language and geography hard to isolate from one another as drivers behind clustering
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 countriesInclusion of larger samples of websites shall
help disentangle roles of geography and language