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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
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Page 1: Describing audience flow on the internet

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

Page 2: Describing audience flow on the internet

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

Page 3: Describing audience flow on the internet

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

Page 4: Describing audience flow on the internet

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

Page 5: Describing audience flow on the internet

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

Page 6: Describing audience flow on the internet

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

Page 7: Describing audience flow on the internet

WWW Audience Flow in Network Analytic Terms

Schematic of a network based on click-stream data using 3 websites

Page 8: Describing audience flow on the internet

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

Page 9: Describing audience flow on the internet

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

Page 10: Describing audience flow on the internet

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

Page 11: Describing audience flow on the internet

WWW Flows Cluster on Geo-Linguistic Lines

Region

Global

USA

China

Japan

Korea

Brazil

Russia

Page 12: Describing audience flow on the internet

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

Page 13: Describing audience flow on the internet

Chinese Cluster Japanese Cluster

Examples of Geo-Linguistic Clusters

Language and geography hard to isolate from one another as drivers behind clustering

Page 14: Describing audience flow on the internet

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

Page 15: Describing audience flow on the internet

EMAIL: [email protected]: @HARSHT

HTTP: / /HARSHT.WORDPRESS.COM

Questions and Comments Welcome