Exploring the Structure of Government
on the Web

Presentation by Robert Ackland at DISC2013,
12-14 December 2013, Daegu, ...
VOSON Project at the ANU (http://voson.anu.edu.au): Teaching,
research and tool development in areas of computational soci...
Background
Government use of the Internet has rapidly evolved.
● While this evolution has been examined in terms of the
co...
Overall aims of project
●

Aim 1: Assess whether government hyperlink networks reflect
offline institutional structures
Is...
●

Aim 2: Use hyperlink data to assess “nodality” of government (Hood &
Margetts 2007) – is government at centre of inform...
Webometrics (link count analysis)
focus on
egonetworks,
rather than
complete
networks
●
typically only
know attributes
of ...
Today – some methodological aspects
Hyperlink network data collection (VOSON)
●
Network reduction techniques
●
Community s...
Hyperlink network data collection (VOSON)

8
●

Manually identified AU and UK government seed pages (typically, entry pages
to government websites):
AU – 88 pages
● UK...
10
VOSON 2.0 web
interface works with
Firefox, Chrome, Safari,
iPad

VOSON+NodeXL allows
construction and import
of hyperlink...
Network reduction techniques

12
●

Network size (pages):
AU: 1,517,020 nodes (pages)
● UK: 1,588,757 nodes (pages)
●

●

First major network reduction tec...
●

Gephi map UK network – only showing 30K+ nodes with
indegree+outdegree>1 ...not much analytical potential from this
vis...
●

In future work we will be investigating
approaches for removing edges to reveal
the “backbone” of UK and AU government
...
Community structure in
government hyperlink networks

16
Some approaches for 'community'
detection in networks
Modularity maximisation (Lancichinetti &
Fortunato, 2012)
●
Edge-Bet...
The hyperlink networks we have collected
are both directed and weighted (weight
on edge from node i to j are number of
pag...
Edge-Betweenness
We found the Edge-Betweenness
algorithm (as implemented in igraph/R)
does not scale well.
●
In a test run...
Infomap
See: http://www.mapequation.org
● Scales well for large, dense networks
● information theoretic approach - appropr...
First attempt...
Tried Infomap implemented in R/iGraph (v. 0.6.5)
● Results: Not good! Algorithm consistently generated a ...
Second attempt...
Results from Lambiotte and Rosvall (2012) were recently
developed into Infomap algorithm
● This latest c...
17 out of 4571
communities
(44% of all
flow)
23
45 out of 4571
communities
(70% of all flow)
24
Each community is named after the website that has the highest
flow and PageRank in that particular community (i.e. the ‘t...
Preliminary findings
Extremely influential communities form around social media
and blogging platforms
● A massive amount ...
Coding websites

27
●

To understand the structure of government hyperlink networks, we need to
know something about the websites in these net...
Data collection
●

Subset of 'important' websites in the UK network were
coded into discrete policy domains by a human cod...
Text processing
R ‘XML’ package used to clean the HTML
(strip HTML tags, remove white spaces,
remove strange ASCII charact...
Support Vector Machine (SVM)
●

Websites with known policy codes = 2157
SVM ‘training sample’ = 2000
● SVM ‘test sample’ =...
SVM Conclusion

Surprising level of accuracy
●
Future work will involve:
●

More data (will use HTML collected via
VOSON)
...
33
Motivation

2
Research Goal

3
Previous studies
Level

Authors

Result
Small-world effect existed between co-authors and the degree

Newman(2001)

distri...
Previous studies
Level

Authors

Result

Verspagen and

Strategic technology alliances, in the two technology fields of ch...
Brief history of governmental policy for UIG collaboration (‘00~’11)


6
Brief history of governmental policy for UIG collaboration (‘00~’11)




7
Research design

8
Methodology
 Network topological analysis
Measures

Definition

Density
Average degree
Average path
length
Diameter

The ...
Methodology
 Centrality measures
Measures
Degree centrality

Definition
CD(i) = (ΣAi)/(n-1)
* Ai = the number of direct l...
Methodology
 Block modeling

11
Data and network construction
 Data collection and network construction

 75 innovative actors (2010)

12
Results

The number of joint patents


30,000
23,973

25,000
20,000
15,000

12,659

10,000
5,000

4,579
1,368

6,735
3,53...
Results


14
Results

Period

No. of No. Density Clustering Average Average
coefficient degree

(random

network)

links

path length
...
Results

2000-2003
Organi

Degree

zation

2004-2007

Rank Closeness Rank Between Rank

centrality

centrality

Degree

t...
Results


17
Conclusions and discussion
 Conclusions

 Policy implications

18
Contributions

19
20
Fred Phillips
DISC 2013, Daegu

General Informatics LLC

Perspectives on
Triple Helix
Agenda
1. 3-Helix as a meso-level notion
– Epicycle in a grander tech-psych-inst
cycle

2. Speed (differentials) as high-l...
3-Helix papers published in
Technological Forecasting &
Social Change
• Wilfred Dolfsma, Loet Leydesdorff “Lock-in and bre...
In D.S. Oh & F. Phillips (Eds),
Technopolis: Best Practices for Science
and Technology Cities (Springer, 2014)
• E. Becker...
IC2 Model
• Preceded 3-helix by several years
• But only parts were made mathematical (Bard et al)
Ac a d e mi a

Indu st ...
The math of AcademicGovernment-Industry
dynamics is interesting,
but...
It is just part of a bigger picture.
The cycle of innovation and change:
Lab to society & back again
Technological
Innovation
New desires
& dreams

New ways to...
We might think all the elements
move together in an orderly way.
Social Needs
Institutional Change

Technological Change

...
But in a free-market economy,
they do not.
• They continually
engage and
disengage.
• Sometimes they
move each other
only ...
Example: Transportation
• Mobile-web rideshare
services
– Gain VC investment
– Start operations
– Get shut down by city
go...
Example: Health
• An elderly person dies
because he was too proud
to wear
– A medical bracelet
– or
– An emergency signall...
Example: Software
• Record companies and publishers
– Sue student MP3 pirates
– Develop DRP software that further alienate...
Example: More and more often,
social/institutional change outpaces
tech change - or will do so soon.
• In most of the worl...
This can be good.
• Individual creativity
may bloom.
• Mistakes...
– Can be undone
efficiently.
– Don’t necessarily infect...
It (disengagement)can be bad.
• Alienation
• Lack of coordination and cooperation
• Little institutional or organizational...
Speed as the system metric
• Really, speed
differentials among the
sectors.
• A “clutch” and
“transmission” are
needed.
• ...
Not bridging organizations, but
buffering organizations
•
•
•
•
•
•
•
•

Civic groups
Workforce training programs
Economic...
3-Helix as meso-level construct: An
epicycle within the TechnologyPsychology-Institutional dynamic
• Macro: Tech-Psych-Ins...
What causes TOPI* disengagement?
*Technological-Organizational-PsychologicalInstitutional

• Bad marketing, bad market res...
“Engaging” doesn’t mean
“attractive nuisance.”
Intrusive
‘engagement’
Update
this app!
Marketing guru Geoffrey Moore says,
• “People have disengaged, for ... self-preservation.”
– With “consequences for consum...
Moore: Engagement is taking
center stage in business.
• Off-line retailers are using digital interactions/devices in their...
Moore is saying
• Advertising used to be
like this.
– Annoying! Consumers
disengaged.

• Now with social media,
mobile web...
ICT for an Intelligently
Engaged Society?
What kinds of
IT foster
positive,
voluntary
engagement?
Why?
What kinds of IT discourage
it? Why?
People are proud to
participate electronically.
• Fighting crime
– Zapruder film; Rodney King videos

• Supporting favorit...
Source: Ganti et al, Mobile
Crowdsensing: Current State and
Future Challenges.
Micro Level: Workforce
Engagement
• Definition: The measure of whether
employees merely do the minimum required
of them, v...
Current state of worker
engagement
ICT for engagement? Summary
• ICT alone cannot create/sustain engagement.
– Human intervention, via buffering institutions...
For many countries where
central government direction is
the norm, 3-helix thinking is
premature.
• Indonesia, Mongolia
• ...
Big man little man game
In sum, the problem is not disengagement, but mis-engagement
among governments, people,
organizations and products, due to...
SUNY Korea’s research agenda
• Combine social science and computer science...
• To find principles of IT design that more ...
Some Implications
• For IT: Meeting users halfway
• For managers: Engagement plans for
each constituency
• For theorists:
...
The math of AcademicGovernment-Industry
dynamics is interesting,
but...
It is just part of a bigger picture.
An aside: Spatializing
an innovation
diffusion model
F. Phillips, On S-curves and Tipping Points. Tech.
Forecasting & Soci...
References
• http://davidsasaki.name/2013/01/beyond-technology-fortransparency/
• A. Charnes, S. Littlechild and S. Sorens...
감사합니다
Thank you
fred.phillips@stonybrook.edu
fp@generalinformatics.com
A Network Analysis of Web-Citations
Among the World’s Universities
George A. Barnett
Department of Communication
Universit...
Research Aims
• Network Analysis of URL-citations among

– 1,000 universities with greatest presence on WWW (1 million
edg...
Data—Web-Citations
• Web-citations among universities collected using Google
– 2,100 X 2,100 matrix of universities (4,407...
Data--Antecedents
University Level
Physical Location

− Google Maps

Country

− cTLD of website (USA--.edu)

Language of I...
Data--Antecedents
National Level
Total Hyperlinks

− Barnett & Park (2012)

International Internet Bandwidth,
GDP & popula...
Results - Universities
•
•
•
•

Over 9.6 million links among 1,000 universities
Density = .606
Mean # of Links = 24.0; S.D...
Results - Universities
Results - Universities
Results – Clusters of Universities
Cluster

Defining Attributes

1. German, Swiss & Italian, not English, central, low pre...
Results - National
• N = 58 Countries
• Density = .924
• United States most central, followed by Germany, U.K., Canada
– >...
Results – Predicting the Structure of
the University URL-citation Network
• Physical Distance Between Campuses

– QAP Corr...
Results – Predicting University
Centrality in Network --Correlations
Results – Predicting University
Centrality in Network -- Regression
In-degree
R2
F
P

Size (log)
English
Bandwidth
Rating
...
Results – Predicting the Structure of the
URL-citation Network-National Level
• QAP Correlations with National Level Netwo...
Results – Predicting Nation’s Centrality
in Network --Correlations
Results – Predicting National Centrality
in the Network -- Regression
In-degree
.524
33.78
.000

35.12

.670

ß

R2
F
P

O...
Discussion
• So where is academic knowledge produced?

– Primarily at prestigious English speaking institutions in the U.S...
Discussion
• At the national level, the countries formed a single group
centered about the U.S. & the U.K.
• U.S. is the m...
Discussion
• Results are consistent with Seeber, et al. (2012)
– European university hyperlink network displays a
center-p...
Discussion
• Consistent with Ortega & Aguilla (2009)

– “The world-class university network graph is comprised of national...
Discussion
• Global academic community as a self-organizing system
– Academic network may be considered an autopoietic or ...
Discussion
• There are environmental constraints that limit the
possible states into which this system may evolve
• issues...
Thank you!
See:
Barnett, G.A. , Park, H.W., Jiang, K, Tang, C, & Aguillo, I.F., (2013),
“A multi-level network analysis of...
Virtual Knowledge Studio (VKS)

“Webometrics Studies” Revisited
in the Age of “Big Data”
Asso. Prof. Dr. Han Woo PARK
Cybe...
Big data
 The term “big data” refers to “analytical technologies that
have existed for years but can now be applied faste...
http://www.emc.com/leadership/digitaluniverse/iview/executive-summary-a-universe-of.htm
http://www.emc.com/leadership/digitaluniverse/iview/images/impact-ofconsumers-lg.jpg
Data-driven Research that focuses on
extracting meaningful data from technosocio-economic systems to discover
some hidden ...
Introduction


Webometrics is broadly defined as the study of webbased content (e.g., text, images, audio-visual objects,...
• Han Woo Park
- “hidden” and “relational” data about

lots of people as well as the few
individuals, or small groups

• L...
First type of Webometrics
• Hyperlink Network Analysis
- Inter-linkage: who linked to whom matrix
- Co-inlink : a link to ...
Inter-link network analysis diagram among Korean escience sites within public domain

WCU
WEBOMETRICS
INSTITUTE

Mapping t...
Co-inlink network analysis

WCU
WEBOMETRICS
INSTITUTE

Mapping the e-science landscape
In South Korea using the Webometric...
Findings
As seen in Figure 4, the network structure shows a clear butterfly pattern. There is one hub (ghism)
that belongs...
Sociology of Hyperlink Networks of Web 1.0,
Web 2.0, and Twitter
A Case Study of South Korea
Introduction
‣ Online & offline lives ➭ co-constructing (e.g. Beer & Burrows, 2007)
‣ Politicians communicate with their c...
2001

2000

‣ 59 isolated in 2000
‣ more centralised in 2001
‣ network of 2001 ➭ a ‘star’ network
- might affected by poli...
2005

2006

‣hubs disappearing
‣easy use of blogs
‣Clear boundaries between different parties
‣strong presence of GNP Asse...
Politician Twitter Network (Following and Mention
Network)
Conclusion

Politicians Twitter Following-follower Network

Politicians Twitter Mention Network
Bi-linked network of politically active
A-list Korean citizen blogs (July 2005)
URI=Centre
DLP=Left
GNP=Right

Just A-list...
Affiliation network diagram using pages
linked to Lee’s and Park’s sites

N = 901 (Lee: 215, Park: 692, Shared: 6)
Tweets on the name of S. Korea president

20
Viewertariat Networks:

A Study of the 2012 South Korean Presidential Debate

Park’s network

Moon’s network
Reply-To Networks of Park’s & Moon’s
Facebook page visitors during TV debates
“Those studies perpetuate the idea that linking
behaviour is not random, and that links are ‘socially
significant in some ...
Park and his colleagues were
extensively cited: 9 times!
•
•
•
•
•
•
•
•
•

Barnett GA, Chung CJ and Park HW (2011) Uncove...
A comment from those who are
NOT doing a hyperlink analysis
• In a chapter of The Sage Handbook of
Online Research Methods...
A threat to Webometrics
• The key application in this area is to collect
some incoming, outgoing, inter-linking, and
co-li...
http://cybermetrics.wlv.ac.uk/Que
riesForWebometrics.htm
A new proposal
• Mike Thelwall
- URL citation searches with the Bing search
API facilities
• Liwen Vaughan
- Incoming hype...
A new proposal : SEO Tools
•
-

Search Engine Optimization Tools
http://www.majesticseo.com/
http://www.opensiteexplorer.o...
Webometrics Ranking of
World Universities
The link visibility data is collected from the two
most important providers of t...
Interlinkage among world universities
• Barnett, G.A., Park, H. W., Jiang, K., Tang, C.,
& Aguillo, I. F. (2013 forthcomin...
Intentional inattention
among Information Scientists?
• Robert Ackland (2013). Web Social Science.

- http://voson.anu.edu...
Let us move to Web Visibility Analysis
Frequently occurring key words in e-science webpages in Korea

Created on Many Eyes...
Websites retrieved more than two times

Note: Websites are larger according to their frequency of retrieval; however, heir...
2nd type of Webometrics: Web Visibility


Web visibility as an indicator of online political power



Presence or appea...
Results – Web Visibility (co-occurrence)
Results – Correlation & Path Analysis
Correlation
1 (N=278)
1 Finance

2 (N=278)

3 (N=234)

1

0.420**

0.101

1

0.184**...
Results – QAP Correlation
1
1 Committee
2 Constituency

2

3

1

0.004

-0.016

1

3 Party
4 Gender
5 Age
6 Incumbent
7 We...
e-리서치 도구의 활용: 웹가시성 분석


블로그 공간에서 후보자들의 웹가시성 수준과 득표 수간
에 밀접한 상관성을 나타냄. (임연수, 박한우, 2010, JKDAS)
실제 득표수
29,120

평균 블로그 수
19,...
2009년 10월 28일 재보선 결과
- 당선자 모두 블로그 가시성 높음
I. 소셜 미디어의 특징 및 영향력
10.26 재보궐 선거 사례
•

(2)

페이스북에서 이름이 동시에 언급되는 이름 연결망을 구성
하여 분석

•

초반에는 두 후보자가 비슷하게 언급되다가,
중반에 접어들자 박원순 ...
I. Semantic network에서 중심성 비교
10.26 재보궐 선거 사례

(2)
•

서울시장 선거 관련 메세지들의 내용
을 분석하여 나오는 단어들의 빈도
분석

•

초반부터 나경원 후보는 빈도가 떨어
지다가...




As Lim & Park (2011, 2013)
claim, the use of web
mentions of politicians’
names is particularly useful
for hierarchi...


Taleb (2012) argues that society
can be conceived as a complex
fabric consisting of the extended
disorder family includ...


In social and communication
sciences, entropy-based
indicators have been widely
used for exploring entropy
values gener...
Mapping Election Campaigns Through Negative Entropy:

Triple and Quadruple Helix Approach
to Korea’s 2012 Presidential Ele...
Introduction


To better understand the dynamics of the 2012 presidential election
in Korea, this study estimates the web...
Literature Review
The total probabilistic entropy (uncertainty) produced by changes in one or
two dimensions is always pos...
Method: Data collection







The number of hits for each search query per media
channel (Facebook, Twitter, and Goog...
SNS 미디어에 따른 중심성에 따른 시각화
Literature Review
Twitter can be very effective to amplify messages particularly in terms of their
one-to-many mode of com...
Literature Review





The mode of information sharing on Facebook differs from that on Twitter.
Facebook functions as ...
Research questions


Therefore, it is important to examine what (social) media
conversations are more likely to generate ...
Method: Measuring (negative) entropy


Figure 1. Binary Entropy Plot


Entropy values (expressed as T for transmission)
for bilateral relationships are, by definition,
positive. Here T is de...


On the other hand, T values for trilateral and quadruple
relationships can be negative, positive, or zero depending on ...
Results


Figure 2. Entropy Values Across Media Channels and Time Periods
Results


Figure 3. T Values for Bilateral and Trilateral Relationships on November 3.
Results


Figure 4. T Values for Bilateral Relationships between Park and Moon
Discussion and conclusions






Twitter has scored the most negative entropy
values and Facebook followed. Google came...
Discussion and conclusions
PARK’s entropy has been slightly higher on
Google than her liberal challenger MOON.
 Park was ...
Paper-code

Keynote Speech

“Creativity and TRIZ”for the Knowledge Network
Analysis in the Emerging Big Data Research”

- ...
Curriculum Vitae

Paper-code

December 14, 2013

Professor emeritus Jae H. Park, Ph.D
-

Professor Emeritus , Industrial a...
Paper-code

<International Consulting and Training>
 Samsung Electronics; Creativity and Innovation “Change Begins with M...
Paper-code

<International Network>
 Center for Creative Leadership, Partner, Liscencee, North Carolina, USA
 SPGR Consu...
TRIZ Founder

G. S. Altshuller
(1926~1998)

Father of TRIZ
Global TRIZ Conference 2013 | www.koreatrizcon.kr
Seoul Trade E...
Paper-code

What is TRIZ ?

TRI Z is a tool for Thinking
but not instead of thinking

G. Altshuller
Change of major discipline

Paper-code
Paper-code

From Tools to Subjects
 Labor : Human

Robot

Creativity
Paper-code

TRIZ
6 Sigma
CAE
Innovation in Global companies

Paper-code
Paper-code

1.
2.
3.
4.
5.
6.
7.

Toyota Method
QFD
TOC
TRIZ
6 Sigma
Taguchi Method
7 Tools of Product
Design
Paper-code
Paper-code

 Research

Areas

◦ Understanding creative cognition and
computation
◦ Creativity to stimulate breakthrough i...
Paper-code

 INSA





Strasbourg

http://www.insa-strasbourg.fr/en/news/news.html
Advanced Master of Innovative Desi...
Paper-code

2008.
11. 28
Edison and Altshuller
•
•
•
•
•

Everybody can be a Inventor
TRIZ Diffusion; No cost
Developed TRIZ in Prison
Benevolent M...
Paper-code

 TRIZ
 Analyzed

many Patents
 By Creative Problem Solving
Methods
 Inductive Research Methods
Paper-code
Paper-code

Various views on TRIZ
•
•
•
•
•
•
•

From Knowledge Management
From 6 Sigma
From Engineering Design
From Innov...
Paper-code
Paper-code

TRIZ as a Science
Technical
Systems

Social
Systems

Natural
Systems

TRIZ
N&A Narbut, 2003
Paper-code

5 Levels of Invention
① Apparent Solution (32%)

①
- Simple
② Simple Improvement within current system
(45%)
③...
Paper-code

Effects in TRIZ
Effects
Systematized
Information funds

Trends

Su-F

Development

Models

ARIZ,
Standards

N&...
Paper-code

Common Approach

TRIZ

Innovation involves the
creation of new ideas

Innovation involves
adapting existing id...
Paper-code

Korea; Creative Economy via
Creativity : Expansion & Convergence

Pie

Bibimbap

- 2/10 -
Creativity and TRIZ

Paper-code

*

Korea Academic TRIZ Association

Industry-Academia Knowledge sharing
Contributor for i...
Paper-code

Main Activities
Expanded use of
TRIZ and social
contribution

Evolution

Nurturing
creative talent

MATRIZ & K...
TRIZ Activities in Korea

Paper-code

Company : Development of Innovative Products,
Problem-Solving and Patents Creation

...
TRIZ Activities in Korea

Paper-code

University : Utilizing TRIZ in subject of “Creative design”
POSTECH
 Master course ...
Paper-code





Systematic innovator
Learn and practice by yourself.
Participate as a member of TRIZ
Association(Daegu-...
Paper-code

Recognition that
 (technical) systems evolve
 Towards the increase of ideality
 By overcoming Contradiction...
Paper-code

GRCIOP Global Network
ICEDR(International Consortium for Executive
Development Research(USA)
Global Integratio...
The Geopolitics of New Media
RANDY KLUVER
TEXAS A&M UNIVERSITY
The context
 The rise of “new media” has transformed politics,

economics, and societies.
 But, “Internet Studies” as a ...
The Big Picture
Issue 1: The implications of a “networked” globe
on geopolitics
 Shifting configurations of influence


Networked, rathe...
Example: Influential players in discourse
surrounding the Egyptian coup weren’t Egyptian!
Saudi #2
But where was the Muslim Brotherhood?
Constraints on global networks
 Language
 Technological diffusion
 Domestic politics/economic priorities
 Platforms/ap...
Should networks follow language groups?
English as the dominant carrier of global
conversation
Internet languages
A new bi-polar world?
Peer to Peer Diplomacy:
Global Social Network Usage
Twitter’s global web traffic
(not counting sms, im, etc)
P2PD:
China’s exclusion from “facebook friendships”
South Korea’s facebook friendships
Russia’s Facebook friendships
Iran’s Facebook friendships
Public Diplomacy: Twitter targets
China’s Twitter outreach
Russia’s Twitter Outreach
Public Diplomacy: E-diplomacy index
How is China doing?
South Korea’s E-Diplomacy
Issue 2: Information Access/Control
 Crowd Sourced
 Unprecedented access to sensitive information
 Stratified
 Customi...
Wikileaks: Crowd-sourced espionage or
invaluable public service?
 Revealed US war plans

and operations, as well as
diplo...
The value of geographic knowledge
Need a drone?
Issue Three: Policies
 Re-articulation of “national interest”
 Alec J. Ross and “21st Century Statecraft”
 “addresses n...
The Internet Freedom Agenda
 “Countries that restrict free access to information or

violate the basic rights of internet...
Final thoughts…..
 We need far more sustained attention to the impact

of new media in between states, as well as within
...
A project from the Social Media Research Foundation: http://www.smrfoundation.org
About Me
Introductions
Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
htt...
Social Media Research Foundation
http://smrfoundation.org
Social Media Research Foundation
People

Disciplines

Institutions

University
Faculty

Computer Science

University of Ma...
What we are trying to do:

Open Tools, Open Data, Open Scholarship
• Build the “Firefox of GraphML” – open tools for
colle...
What we have done: Open Tools
• NodeXL
• Data providers (“spigots”)
–
–
–
–
–
–
–
–

ThreadMill Message Board
Exchange Ent...
What we have done: Open Data
• NodeXLGraphGallery.org

– User generated collection
of network graphs,
datasets and annotat...
What we have done: Open Scholarship
What we have done: Open Scholarship
Social Media
(email, Facebook, Twitter,
YouTube, and more)
is all about
connections
from people
to people.
10
Patterns are
left behind
11
There are many kinds of ties….

Send, Mention,

Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, ...
Social Network Theory
http://en.wikipedia.org/wiki/Social_network
• Central tenet

– Social structure emerges from
– the a...
SNA 101
• Node

A

– “actor” on which relationships act; 1-mode versus 2-mode networks

• Edge
B

– Relationship connectin...
NodeXL
Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graph
theory as easy as a pie chart, with integr...
Now Available
Communities
in Cyberspace
Goal: Make SNA easier
• Existing Social Network Tools are challenging
for many novice users
• Tools like Excel are widely ...
http://www.flickr.com/photos/badgopher/3264760070/
http://www.flickr.com/photos/druclimb/2212572259/in/photostream/
http://www.flickr.com/photos/hchalkley/47839243/
http://www.flickr.com/photos/rvwithtito/4236716778
http://www.flickr.com/photos/62693815@N03/6277208708/
Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.
NodeXL
Network Overview Discovery and Exploration add-in for Excel 2007/2010

A minimal network can
illustrate the ways di...
Hubs
Bridges
http://www.flickr.com/photos/storm-crypt/3047698741
Welser, Howard T., Eric Gleave, Danyel Fisher,
and Marc Smith. 2007. Visualizing the Signatures
of Social Roles in Online ...
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
http://www.flickr.com/photos/amycgx/3119640267/
#teaparty
15 November 2011

#occupywallstreet
15 November 2011

http://www.newscientist.com/blogs/onepercent/2011/11/occup...
Like MSPaint™ for graphs.
— the Community

Introduction to NodeXL
NodeXL Ribbon in Excel
NodeXL
data import sources
Example NodeXL data importer for Twitter
NodeXL imports “edges” from social media data sources
NodeXL displays subgraph images along with network metadata

NodeXL creates a list of “vertices” from imported social medi...
NodeXL
Automation
makes analysis
simple and fast

Perform
collections
of common
operations
with a single
click
NodeXL Generates Overall Network Metrics
50
51
52
53
54
55
56
57
58
Divided
Polarized

Unified
In-group

Fragmented
Brand

Clustered
Communities

In-Hub & Spoke
Broadcast

Out-Hub & Spoke
Su...
6 kinds of Twitter social media networks
#My2K

Polarized
#CMgrChat

In-group / Community
Lumia

Brand / Public Topic
#FLOTUS

Bazaar
New York Times Article
Paul Krugman

Broadcast: Audience + Communities
Dell Listens/Dellcares

Support
SNA questions for social media:
1.
2.
3.
4.

What does my topic network look like?
What does the topic I aspire to be look...
Twitter Network for “Microsoft Research”
*BEFORE*
Twitter Network for “Microsoft Research”
*AFTER*
Network Motif Simplification

Cody Dunne, University of Maryland
Network Motif Simplification

D-connector (glyph on the right)

Fan(glyph on the right)

D-clique (glyphs for 4, 5, and 6
...
NodeXL
Graph Gallery
Scholars using NodeXL
• Communications
– Katy Pearce
– Itai Himelboim

• Business
– Scott Dempwolf

• Humanities/Classics
...
C. Scott
Dempwolf,
PhD

Research Assistant
Professor & Director
UMD - Morgan State
Center for Economic
Development
What is Social Network Analysis?
How is it useful for the humanities?

1. New framework for analysis
2. Data visualization...
NodeXL calculates metrics
about networks and content
The Content summary
spreadsheet displays the most
frequently used URLs, hashtags,
and user names within the
network as a w...
NodeXL Graph Gallery

80
NodeXL as a Research Tool

81
NodeXL as a Teaching Tool
I. Getting Started with Analyzing Social Media Networks
1. Introduction to Social Media and Soci...
What we want to do:
(Build the tools to) map the social web
• Move NodeXL to the web: (Node[NOT]XL)
– Node for Google Doc ...
NodeXL Results
• Easy to learn, yet powerful and insightful
• Widely used by both students and researchers

• Free and ope...
How you can help
Sponsor a feature
Sponsor workshops
Sponsor a student
Schedule training
Sponsor the foundation
Donate you...
Available Now in NodeXL!
•
•
•
•
•
•
•
•
•
•
•
•
•

Motif Simplification
Group-in-a-Box Layouts
Data import spigots
Excel ...
Strategies for social media engagement based on
social media network analysis
A project from the Social Media Research Foundation: http://www.smrfoundation.org
International Collaboration &
Green Technology Generation
Assessing the East Asian
Environmental Regime
Matthew A. Shapiro...
Impetus
• Shapiro and Nugent (2012) “Institutions and the
sources of innovation” in IJPP

• Total factor productivity is h...
International
institutions
To other regions

To other regions

Regional institutions

Country 2 FDI

Country 2
ecologists
...
International
institutions
To other regions

To other regions

Regional institutions

Country 2 FDI

Country 2
ecologists
...
International
institutions
To other regions

To other regions

Regional institutions

Country 2 FDI

Country 2
ecologists
...
Research Questions
• Are the Northeast Asian countries key
collaborators in pursuit of green R&D?
• Yes, particularly in r...
Green R&D
• Patents
• IPC Green Inventory
•
•
•
•
•
•
•

Alternative energy production
Transportation
Energy conservation
...
Alternative energy production
• Biofuels
• Integrate gasification combined cycle
• Fuel cells
• Pyrolysis or gasification ...
Data Collection
• Source: USPTO
• Collection method: Leydesorff’s tools
• Unit of analysis: country of inventor
Data Description
IL
BE

• Dates: 1990-2013
• 129,640 total inventors

IN

IT

CN

CH

NZ TW
all others

AU
KR
DK

• Assump...
Are Northeast Asian countries key collaborators?
All years: 1990-2013
Longitudinal analysis…
1990-1997
1998-2004
2005-2013
Is Northeast Asia a singular research hub?
All years: 1990-2013
Longitudinal analysis…
1990-1997
1998-2004
2005-2013
Small world example
Northeast Asia only: 1990-2013
Implications
• Empirical
• R&D collaboration can be beneficial from both
intra- as well as extra-regionally. Both are
happ...
Assessing Social Media Coverage in
Japan: Before and After March 11, 2011

Leslie M. Tkach-Kawasaki
University of Tsukuba
...
Overview
1.
2.
3.
4.
5.
6.

Introduction: Social Media in Japan
2010-2011
March 11, 2011: Triple Disaster
Social Media: Be...
Japan’s Internet Population 2011

Source: 2011 情報通信白書平成23年版
Social Media in Japan 2010-2011
Have used the following at least once…..
Blogs  77.3%
Video-sharing websites  62.8%
SNS ...
The Year in Social Media 2010-11
International diplomacy:Youtube and Chinese
fishing vessel (September 2010)
 Entertainme...
And March 11, 2011….
Information Provision/Gathering During
2011 Earthquake

Source: 2012 White Paper on Information and Communications in Japa...
Research question….

Are there perceivable differences
in the discourse (phrases) about
social media in Japan’s
newspaper ...
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  1. 1. Exploring the Structure of Government on the Web Presentation by Robert Ackland at DISC2013, 12-14 December 2013, Daegu, South Korea Robert Ackland (Australian National University) Paul Henman (University of Queensland) Tim Graham (University of Queensland) Homepage: https://researchers.anu.edu.au/researchers/ackland-rj Project: http://voson.anu.edu.au
  2. 2. VOSON Project at the ANU (http://voson.anu.edu.au): Teaching, research and tool development in areas of computational social science, network science, web science since 2003 2
  3. 3. Background Government use of the Internet has rapidly evolved. ● While this evolution has been examined in terms of the content, usability and interactivity of sites, the institutional structure of government on the web is less explored. ● Australian Research Council-funded project titled "The institutional structure of e-government: a cross-policy, cross-country comparison" (Henman, Ackland, Margetts) ● 3
  4. 4. Overall aims of project ● Aim 1: Assess whether government hyperlink networks reflect offline institutional structures Is e-government facilitating joined-up government or are jurisdictional boundaries still a significant barrier? ● Whalen (2011) studied the hyperlink structure of the US .gov domain, assessing correspondence between online structure of US government and its offline hierarchy. ● ● Major difference is our project compares the UK and Australia, identifying both similarities and contrasts in the relationship between institutional structure and online presence. 4
  5. 5. ● Aim 2: Use hyperlink data to assess “nodality” of government (Hood & Margetts 2007) – is government at centre of informational networks on Web? Nodality affects whether government messages received by the population. ● Web might increase government nodality, but can also decrease nodality, through increased competition from other information providers (who may destabilise/confuse/subvert the messages and actions of government). Example: anti-vaccination lobby groups. ● We ask: is government using the web to enhance its visibility? Are there differences in nodality across policy domains, countries (AU and UK)? ● Our approach is different to that used by Escher et al. (2006) ● ● ● Escher et al. focused only on the UK Foreign Office (and US and Australian counterparts), our analysis includes other sectors of government, allowing crosscountry and cross-sector comparisons We collect more hyperlink data, allowing us to identify the connection between sites that link to (or are linked to by) government sites. We can construction of nodality measures that are different to those used by Escher et al. (e.g. those requiring complete network data). 5
  6. 6. Webometrics (link count analysis) focus on egonetworks, rather than complete networks ● typically only know attributes of ego, not alters ● 6
  7. 7. Today – some methodological aspects Hyperlink network data collection (VOSON) ● Network reduction techniques ● Community structure in government hyperlink networks ● Coding websites (machine learning) ● 7
  8. 8. Hyperlink network data collection (VOSON) 8
  9. 9. ● Manually identified AU and UK government seed pages (typically, entry pages to government websites): AU – 88 pages ● UK – 92 pages ● ● Used the VOSON software (http://voson.anu.edu.au) to construct hyperlink network data using two stage approach: ● Stage 1: ● ● ● Stage 2: ● ● VOSON in-built crawler crawled the seed sites finding internal pages linked to from the entry page. Collected outbound links from each of the internal pages and also text content Bing API was used to find all inbound links to each of the internal pages (including seed page) Every new page discovered above (i.e. pages that either link to or are linked to by government web page) was then crawled by VOSON in-built crawler to find connections among these pages Data collected in 2012 9
  10. 10. 10
  11. 11. VOSON 2.0 web interface works with Firefox, Chrome, Safari, iPad VOSON+NodeXL allows construction and import of hyperlink networks from within NodeXL 11
  12. 12. Network reduction techniques 12
  13. 13. ● Network size (pages): AU: 1,517,020 nodes (pages) ● UK: 1,588,757 nodes (pages) ● ● First major network reduction technique: construct network of websites rather than pages VOSON has approach for automatically grouping pages into “pagegroups” ● e.g for AU, 6694 pages from Australian Taxation office all included in a single node “ato.gov.au” ● ● Full network size (pagegroups/sites): AU: 110665 nodes (pages), 290031 edges ● UK: 109161 nodes (pages), 280580 edges ● 13
  14. 14. ● Gephi map UK network – only showing 30K+ nodes with indegree+outdegree>1 ...not much analytical potential from this visualisation... 14
  15. 15. ● In future work we will be investigating approaches for removing edges to reveal the “backbone” of UK and AU government hyperlink networks ● e.g. Serrano, M., Boguñá, M. and A. Vespignani (2009): “Extracting the multiscale backbone of complex weighted networks,” PNAS, 106(16), 6483-6488. 15
  16. 16. Community structure in government hyperlink networks 16
  17. 17. Some approaches for 'community' detection in networks Modularity maximisation (Lancichinetti & Fortunato, 2012) ● Edge-Betweenness (Girvan & Newman, 2001) ● Fast-Greedy (Clauset et al, 2004) ● Multi-Level (Blondel et al, 2008) ● Walktrap (Pons & Latapy, 2005) ● Infomap (Rosvall, Axelsson & Bergstrom, 2009) ● 17
  18. 18. The hyperlink networks we have collected are both directed and weighted (weight on edge from node i to j are number of pages with links from site i to j) ● Of the above, only Edge-Betweenness and Infomap support directed and weighted graphs ● 18
  19. 19. Edge-Betweenness We found the Edge-Betweenness algorithm (as implemented in igraph/R) does not scale well. ● In a test run with UK hyperlink network, algorithm did not converge after 24 hours running... ● 19
  20. 20. Infomap See: http://www.mapequation.org ● Scales well for large, dense networks ● information theoretic approach - appropriate to this network, where there is flow of information and attention ● If site i links to site j can think of a flow of information from j to i and a flow of attention from i to j. ● We do not have data on flow of web users from site i to site j i.e. 'clickstream data' ● We therefore make assumption that the number of pages on site i that contain hyperlinks to site j (these are our edge weights) is proportional to the flow of attention/information ● 20
  21. 21. First attempt... Tried Infomap implemented in R/iGraph (v. 0.6.5) ● Results: Not good! Algorithm consistently generated a single massive community (approx. 95% of nodes) and thousands of tiny communities (1 or 2 nodes per community) ● Results do not pass ‘sanity test’ (i.e. face validity) ● The problem: ● Many nodes in the UK network have no outlinks ● Therefore, effect of teleportation in the Infomap algorithm is significant (it randomly connects nodes) ● This problem was solved in Lambiotte and Rosvall (2012) ● 21
  22. 22. Second attempt... Results from Lambiotte and Rosvall (2012) were recently developed into Infomap algorithm ● This latest code is not yet integrated in R/iGraph ● So, next steps: ● Download and compile C++ source code for Infomap (v. 0.12.13) ● http://www.mapequation.org/code.html ● Run the standalone Infomap algorithm ● ● Using Infomap Map Generator, can examine the community structure of UK network at different scales (varying the number of communities displayed and number of links between communities) 22
  23. 23. 17 out of 4571 communities (44% of all flow) 23
  24. 24. 45 out of 4571 communities (70% of all flow) 24
  25. 25. Each community is named after the website that has the highest flow and PageRank in that particular community (i.e. the ‘top dog’ website) ● Distribution of flow across network follows a power law ● There are many communities, but a very small percentage ‘hog’ all the flow across the network ● Top 5% of communities (229 nodes out of 4571) account for about 86% of all flow in the network ● ● Infomap uses an implementation of the PageRank algorithm to calculate ‘importance’ of each community (aggregate PageRank of all websites in that community) 25
  26. 26. Preliminary findings Extremely influential communities form around social media and blogging platforms ● A massive amount of flow is directed through the ‘Twitter’ community (e.g. from Twitter to www.parliament.uk) ● Many UK seed sites form influential communities (i.e. Top 20), but not all. ● Somewhat unexpectedly, two UK Gov ‘business’ websites each form highly influential communities ● http://www.direct.gov.uk (community rank #4, 0.048% of all flow throughout network) ● http://bis.gov.uk (community rank #8, 0.025% of all flow throughout network) ● 26
  27. 27. Coding websites 27
  28. 28. ● To understand the structure of government hyperlink networks, we need to know something about the websites in these networks ● ● Generic top-level domains (.edu, .com, org etc.) will only give very coarsegrained information on who these sites are ● ● What policy domain are they in? (health, education, social security?) This is social science research so we need more information on nodes Options: 1. Manually code every site (not feasible, as we have >100K sites) 2. Manually code a subset of sites e.g. the “most important” sites based on centrality measure (scientifically valid?) 3. Manually code a sample of sites (e.g. adaptive sampling). To be explored in future... 4. Manually code training dataset and then use machine learning to predict website type ● The following is summary of preliminary work on approach 4... 28
  29. 29. Data collection ● Subset of 'important' websites in the UK network were coded into discrete policy domains by a human coder Subset chosen as seed sites plus sites connected to two or more seed sites ● e.g. coding: ‘Community services’, ‘Health’, ‘Foreign Affairs’ ● Need to collect and ‘clean’ the HTML data from websites in the network ● While the original VOSON crawl collected text content for all websites crawled, for this proof of concept, we re-collected the text content (in future we will use the VOSON-collected text data) ●
  30. 30. Text processing R ‘XML’ package used to clean the HTML (strip HTML tags, remove white spaces, remove strange ASCII characters, convert to lowercase, extract key word frequencies) ● 2157 websites were usable (i.e. with ‘clean’ web text and a known policy domain) ● Machine Learning using the ‘RTextTools’ package in R (supervised learning for text classification) ●
  31. 31. Support Vector Machine (SVM) ● Websites with known policy codes = 2157 SVM ‘training sample’ = 2000 ● SVM ‘test sample’ = 157 ● ● Some example results of classification: PRECISION RECALL F-SCORE Education 0.94 0.83 0.88 Employment 1.00 0.14 0.25 Environment 0.99 0.79 0.88 Foreign Affairs 1.00 0.44 0.61 Health 0.52 0.97 0.68 Housing 0.96 0.79 0.87
  32. 32. SVM Conclusion Surprising level of accuracy ● Future work will involve: ● More data (will use HTML collected via VOSON) ● Investigate different machine learning algorithms ●
  33. 33. 33
  34. 34. Motivation 2
  35. 35. Research Goal 3
  36. 36. Previous studies Level Authors Result Small-world effect existed between co-authors and the degree Newman(2001) distribution roughly follows the power law in co-authorship networks in the fields of physics, biomedicine and computer science Barabasi et al. (2002) Ramasco et al. (2004) Co-authorship network in mathematics and neuroscience is scale-free, and the network evolution is characterized by preferential attachment. Co-authorships network in the field of condensed matter showed that the degree distribution follows a power law. Individual Co-authorship network in the field of genetic programming changes Researcher Tomassini and Luthi (2007) in accordance with preferential attachment level International co-authorship grew based on the principle of Wagner and Leydesdorff (2005) preferential attachment, although the attachment mechanism was not fitted to a pure power law. Moody (2004) Brantle and Fallah (2011) Co-authorship network in sociology does not have a small-world structure. Collaboration network of patent inventors has a scale-free power law property. 4
  37. 37. Previous studies Level Authors Result Verspagen and Strategic technology alliances, in the two technology fields of chemicals Duysters (2004) Powell et al. (2005) Organization level Gay and Dousset (2005) Barber et al. (2006) Breschi and Cusmao (2004) and food, could be characterized as small worlds. The alliance network among dedicated biotech firms is scale-free. The alliance network in the biotechnology industry has a small-world effect with a scale-free property based on preferential attachment. Both studies reported the existence of small-world and scale-free property in inter-organizational R&D relationships from EU-FP Programmes data. 5
  38. 38. Brief history of governmental policy for UIG collaboration (‘00~’11)  6
  39. 39. Brief history of governmental policy for UIG collaboration (‘00~’11)   7
  40. 40. Research design 8
  41. 41. Methodology  Network topological analysis Measures Definition Density Average degree Average path length Diameter The largest geodesic path length in the network Clustering coefficient Degree centralization Power law distribution 9
  42. 42. Methodology  Centrality measures Measures Degree centrality Definition CD(i) = (ΣAi)/(n-1) * Ai = the number of direct links of node i, * n = the total number of nodes Closeness centrality CC(i) = (n-1)/(ΣDij) * Dij = the number of links in the geodesic linking node i and node j * n = the total number of nodes Betweenness centrality CB(i)=[Σj<k gjk(i)/gjk]/[(n-1)(n-2)/2] * gjk = the number of geodesics linking node j and node k * gjk(i) = the number of geodesics linking node j and node k that contain node i * n = the total number of nodes 10
  43. 43. Methodology  Block modeling 11
  44. 44. Data and network construction  Data collection and network construction  75 innovative actors (2010) 12
  45. 45. Results The number of joint patents  30,000 23,973 25,000 20,000 15,000 12,659 10,000 5,000 4,579 1,368 6,735 3,535 5,720 2004-2007 2008-2011 10,623 0 2000-2003 2000-2011 Year 13
  46. 46. Results  14
  47. 47. Results  Period No. of No. Density Clustering Average Average coefficient degree (random network) links path length (random nodes of Diameter Degree centralization network) Power-law distribution Power-law KS p- exponent statistic value 2000~2003 46 90 0.087 0.323 (0.069) 1.957 2.997 (2.919) 7 0.351 2.768 0.193 0.03 2004~2007 61 209 0.114 0.375 (0.125) 3.410 2.366 (2.310) 5 0.331 2.924 0.138 0.05 2008~2011 60 387 0.219 0.498 (0.213) 6.450 1.933 (1.827) 4 0.493 3.305 0.115 0.23 15
  48. 48. Results  2000-2003 Organi Degree zation 2004-2007 Rank Closeness Rank Between Rank centrality centrality Degree tion ness Organiza 2008-2011 Rank Closeness Rank Between Rank centrality centrality centrality Degree tion ness Organiza centrality Rank Closeness Rank Between Rank centrality ness centrality centrality SEC 0.422 1 0.506 2 0.253 2 ETRI 0.433 1 0.594 1 0.155 1 SNU 0.695 1 0.756 1 0.144 1 ETRI 0.378 2 0.479 3 0.252 3 SEC 0.400 2 0.583 2 0.104 4 KAIST 0.593 2 0.702 2 0.112 2 KAIST 0.289 3 0.511 1 0.241 4 SNU 0.350 3 0.577 3 0.146 2 YSU 0.559 3 0.686 3 0.043 5 KRICT 0.200 4 0.421 7 0.049 HYU 0.333 4 0.571 4 0.118 3 KRU 0.542 4 0.686 3 0.052 4 HMC 0.178 5 0.437 5 0.290 1 KAIST 0.283 5 0.522 10 0.082 8 HYU 0.492 5 0.656 5 0.076 3 POST ECH 0.156 6 0.421 7 0.084 9 YSU 0.267 6 0.536 6 0.094 6 ETRI 0.475 6 0.634 6 0.042 6 LGE 0.156 6 0.446 4 0.078 10 HMC 0.250 7 0.526 7 0.097 5 SEC 0.458 7 0.634 6 0.037 9 CII 0.156 6 0.402 10 0.013 KRU 0.250 7 0.545 5 0.092 7 POST ECH 0.424 8 0.615 9 0.029 KICT 0.111 9 0.360 0.136 5 SKKU 0.217 9 0.526 7 0.051 9 SKKU 0.407 9 0.621 8 0.039 8 KIMM 0.111 9 0.395 0.104 7 POST ECH 0.217 9 0.526 7 0.031 HMC 0.373 10 0.602 10 0.034 10 KIST 0.111 9 0.437 5 0.046 KT 0.183 0.517 0.010 KIST 0.356 0.602 10 0.010 KT 0.111 9 0.409 9 0.003 LGE 0.167 0.458 0.042 IHU 0.322 0.578 0.030 HMB 0.044 0.319 0.127 6 KHU 0.167 0.500 0.038 CAU 0.322 0.590 0.018 KHNP 0.067 0.249 0.087 8 KRICT 0.133 0.455 0.021 KRICT 0.305 0.578 0.041 7 10 16
  49. 49. Results  17
  50. 50. Conclusions and discussion  Conclusions  Policy implications 18
  51. 51. Contributions 19
  52. 52. 20
  53. 53. Fred Phillips DISC 2013, Daegu General Informatics LLC Perspectives on Triple Helix
  54. 54. Agenda 1. 3-Helix as a meso-level notion – Epicycle in a grander tech-psych-inst cycle 2. Speed (differentials) as high-level system metric – Roles of buffering institutions and ICT – Need for smart engagement 3. Applying 3-helix in the developing world 4. SUNY Korea’s joint TS/CS research
  55. 55. 3-Helix papers published in Technological Forecasting & Social Change • Wilfred Dolfsma, Loet Leydesdorff “Lock-in and break-out from technological trajectories: Modeling and policy implications,” 76( 7), Sept. 2009, 932-941. • Raul Gouvea, Sul Kassicieh, M.J.R. Montoya “Using the quadruple helix to design strategies for the green economy,” 80(2), Feb. 2013, 221-230. • Øivind Strand, Loet Leydesdorff “Where is synergy indicated in the Norwegian innovation system? Triple-Helix relations among technology, organization, and geography,” 80(3), Mar. 2013, 471-484. • Inga A. Ivanova, Loet Leydesdorff “Rotational symmetry and the transformation of innovation systems in a Triple Helix of university– industry–government relations,” In Press, Corrected Proof, Available online 19 Sept. 2013.
  56. 56. In D.S. Oh & F. Phillips (Eds), Technopolis: Best Practices for Science and Technology Cities (Springer, 2014) • E. Becker, B. Burger and T. Hülsmann, “Regional Innovation and Cooperation among Industries, Universities, R&D Institutes, and Governments” • F. Phillips, S. Alarakhia and P. Limprayoon,“The Triple Helix: International Cases and Critical Summary” • José Alberto Sampaio Aranha, “Arrangement of Actors in the Triple Helix Innovation”
  57. 57. IC2 Model • Preceded 3-helix by several years • But only parts were made mathematical (Bard et al) Ac a d e mi a Indu st ry Go v e r n me n t Com m un it y Talen t Technology Capi t al Kno w - Ho w Ma rke t Ne e ds V alu e - A dd e d Ec ono m ic Deve lop me nt
  58. 58. The math of AcademicGovernment-Industry dynamics is interesting, but... It is just part of a bigger picture.
  59. 59. The cycle of innovation and change: Lab to society & back again Technological Innovation New desires & dreams New ways to organize (Public & private) Note how this schema extends Everett Rogers’ more linear model. New Products & Services New ways to Interact socially New ways of producing and using products & services
  60. 60. We might think all the elements move together in an orderly way. Social Needs Institutional Change Technological Change Psychological Change Organizational Change
  61. 61. But in a free-market economy, they do not. • They continually engage and disengage. • Sometimes they move each other only by friction. • 90% of MOT and Tech Policy problems stem from the differing speeds of the 3 sectors.
  62. 62. Example: Transportation • Mobile-web rideshare services – Gain VC investment – Start operations – Get shut down by city governments trying to regulate them under old taxi rules. • Institutions have changed slower than technology and social demand.
  63. 63. Example: Health • An elderly person dies because he was too proud to wear – A medical bracelet – or – An emergency signaller. • Psychology has changed slower than technology.
  64. 64. Example: Software • Record companies and publishers – Sue student MP3 pirates – Develop DRP software that further alienates customers – Can’t adapt away from paper and CD publishing. • Business organizations change more slowly than technology and social demand.
  65. 65. Example: More and more often, social/institutional change outpaces tech change - or will do so soon. • In most of the world, an excess of funds is chasing too few growth investment opportunities. • Fewer US companies are making IPOs. • Small-government activists rail indiscriminately against direct government monetary support for new technologies. See Phillips (2011).
  66. 66. This can be good. • Individual creativity may bloom. • Mistakes... – Can be undone efficiently. – Don’t necessarily infect the whole system.
  67. 67. It (disengagement)can be bad. • Alienation • Lack of coordination and cooperation • Little institutional or organizational creativity • Waste and pollution • Lives lost
  68. 68. Speed as the system metric • Really, speed differentials among the sectors. • A “clutch” and “transmission” are needed. • The question is less how to engage, but rather, when. • The key is not engagement per se, but smart (well-timed) engagement.
  69. 69. Not bridging organizations, but buffering organizations • • • • • • • • Civic groups Workforce training programs Economic development agencies Technology brokers Open innovation integrators Accountancies Industry associations NGOs The IC2 Model partially captured this. • • • • Incubators Law firms Venture capital TTOs
  70. 70. 3-Helix as meso-level construct: An epicycle within the TechnologyPsychology-Institutional dynamic • Macro: Tech-Psych-Inst • Meso: Aca-Gov-Indus Tech – “Triple Helix” • Micro: – Dynamics within people and within organizations; – Technology life cycles • The buffering institutions span all 3 levels. Inst (3-Helix)
  71. 71. What causes TOPI* disengagement? *Technological-Organizational-PsychologicalInstitutional • Bad marketing, bad market research • Mistrust, bad service • Technology inaccessible to underserved populations • Competition among de facto standards (e.g., VHS vs Beta) • Lack of vision • Poor design of information & communication products and programs.
  72. 72. “Engaging” doesn’t mean “attractive nuisance.”
  73. 73. Intrusive ‘engagement’ Update this app!
  74. 74. Marketing guru Geoffrey Moore says, • “People have disengaged, for ... self-preservation.” – With “consequences for consumer and brand marketing, – “and long-term implications for education, health care, citizen participation, and workforce involvement. • “So engagement is rightfully going to be a big investment theme.”
  75. 75. Moore: Engagement is taking center stage in business. • Off-line retailers are using digital interactions/devices in their in-store experiences. – Example: Starbucks. • “Social marketing foster[s] engagement around topics that ... reflect well upon the sponsor.” – Example: Sephora. • “Big data analytics drive communications that can break through the wall of detachment.” – Example: Obama campaign 2012.
  76. 76. Moore is saying • Advertising used to be like this. – Annoying! Consumers disengaged. • Now with social media, mobile web, Yelp.com, – Consumers share product reviews & complaints. – Advertisers have to treat consumers more gently. – To make us want to continually re-engage. • Engaging doesn’t mean shouting.
  77. 77. ICT for an Intelligently Engaged Society?
  78. 78. What kinds of IT foster positive, voluntary engagement? Why?
  79. 79. What kinds of IT discourage it? Why?
  80. 80. People are proud to participate electronically. • Fighting crime – Zapruder film; Rodney King videos • Supporting favorite businesses, authors – Amazon reviews • For post-disaster aid – Crowd-mapping of post-earthquake Haiti • Crowd-funding research projects and entrepreneurs • Though there are abuses.
  81. 81. Source: Ganti et al, Mobile Crowdsensing: Current State and Future Challenges.
  82. 82. Micro Level: Workforce Engagement • Definition: The measure of whether employees merely do the minimum required of them, versus proactively driving innovation and new value for the organization. • Thus, engagement – “can only ever be partially accounted for by deploying the latest new collaborative technology, – “and probably significantly less than many of its proponents would have you believe.” Source: Hinchcliffe
  83. 83. Current state of worker engagement
  84. 84. ICT for engagement? Summary • ICT alone cannot create/sustain engagement. – Human intervention, via buffering institutions, can achieve ICT-aided engagement. • ICT, especially sensing and crowdsourcing, may assist in deciding when to engage. – Thus achieving smart engagement. • This applies to all 3 levels (macro, meso, micro) of our multi-level Technology & Society diagram.
  85. 85. For many countries where central government direction is the norm, 3-helix thinking is premature. • Indonesia, Mongolia • USA: Industry lobbying government presents a slightly different problem...
  86. 86. Big man little man game
  87. 87. In sum, the problem is not disengagement, but mis-engagement among governments, people, organizations and products, due to: • Speed differentials (i.e., poor timing) • Lack of vision • Poor design of information & communication products and programs. – Lack of feedback – Excess complexity, leading to slow comprehension and adoption – Excess technology push (solutions without problems) – Excess demand pull (unrealistic expectations) – Other factors
  88. 88. SUNY Korea’s research agenda • Combine social science and computer science... • To find principles of IT design that more quickly lead to engagement that is... – Well-timed – Smart – Satisfying • Among – – – – Individuals Businesses Government institutions Technology developers • With secure applications in several techno-policy domains (health, energy, etc.).
  89. 89. Some Implications • For IT: Meeting users halfway • For managers: Engagement plans for each constituency • For theorists: – Modeling the moderating effect of buffering institutions – Impact of coalitions on the 3-helix dynamic
  90. 90. The math of AcademicGovernment-Industry dynamics is interesting, but... It is just part of a bigger picture.
  91. 91. An aside: Spatializing an innovation diffusion model F. Phillips, On S-curves and Tipping Points. Tech. Forecasting & Social Change, 74(6), July 2007, 715-730. Alan M. Turing, The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society of London. B 327, 37–72 (1952) http://www.cgjennings.ca/toybox/turingmorph/
  92. 92. References • http://davidsasaki.name/2013/01/beyond-technology-fortransparency/ • A. Charnes, S. Littlechild and S. Sorensen, “Core-stem Solutions of N-person Essential Games.” Socio-Econ. Plan. Sci. Vol. I, pp. 649660 (1973). • David Watson The Engaged University. Routledge, 2013. • Dion Hinchcliffe, “Does technology improve employee engagement?” Enterprise Web 2.0, Nov. 5, 2013. http://www.zdnet.com/doestechnology-improve-employee-engagement-7000021695/ • Jonathan Bard, Boaz Golany and Fred Phillips, “Bubble Planning and the Mathematics of Consortia.” Third International Conference on Technology Policy and Innovation, Austin, Texas, September, 1999. • F. Phillips, The state of technological and social change: Impressions. Technological Forecasting & SocialChange. 78(6), July 2011, 1072-1078.
  93. 93. 감사합니다 Thank you fred.phillips@stonybrook.edu fp@generalinformatics.com
  94. 94. A Network Analysis of Web-Citations Among the World’s Universities George A. Barnett Department of Communication University of California, Davis gbarnett@ucdavis.edu Daegu Gyeongbuk International Social Network Conference December 12-14, 2013
  95. 95. Research Aims • Network Analysis of URL-citations among – 1,000 universities with greatest presence on WWW (1 million edges) – In 58 different countries – Multi-level analysis (both Universities & Countries) • Antecedent factors that determine the network’s structure – University level − National Level • Physical distance  • Same country  Capacity • Language of instruction  • Size  • Ph.D. granting  • Prestige • Research Excellence (Nobel Prizes) Hyperlink Connections International Bandwidth GDP, GDP/capita International Student Flows Nobel Prizes
  96. 96. Data—Web-Citations • Web-citations among universities collected using Google – 2,100 X 2,100 matrix of universities (4,407,900 cells) generated – search query “university A webdomain” site:university B webdomain "harvard.edu" site:stanford.edu − Not all URL-citations are links, e.g., email addresses in coauthored papers − Removed universities with no ties & the smaller of a university’s multiple domains, retained 1,000 most interlinked Universities − Matrix of inter-citations aggregated to the national level
  97. 97. Data--Antecedents University Level Physical Location − Google Maps Country − cTLD of website (USA--.edu) Language of Instruction − Country of University (India & Singapore—English) Size of University − Europe -- (EUMIDA) (http://thedatahub.org/dataset/eumida) − U.S. -- College Handbook 2012 − Asia, Africa, Oceania, Latin American & Canada – Universities’ Websites Prestige − U.S. News, World’s Best Universities 2012 http://www.usnews.com/education/ Nobel Prizes − (http://www.nobelprize.org)
  98. 98. Data--Antecedents National Level Total Hyperlinks − Barnett & Park (2012) International Internet Bandwidth, GDP & population − TeleGeography (2012) (http://www.telegeography.com/) Student Exchange − UNESCO (http://stats.uis.unesco.org/unesco) International Co-authorships − Leydesdorff & Wagner (2008) International Citations − Science Citation Index
  99. 99. Results - Universities • • • • Over 9.6 million links among 1,000 universities Density = .606 Mean # of Links = 24.0; S.D. = 2,208.6 Greatest # of links (322,000) – Universität Trier & Rheinisch Westfalische Technische Hochschule Aachen, two German institutions that host huge & popular bibliographic systems (DBLP & SunSite)
  100. 100. Results - Universities
  101. 101. Results - Universities
  102. 102. Results – Clusters of Universities Cluster Defining Attributes 1. German, Swiss & Italian, not English, central, low prestige, less bandwidth connections 2. English (U.S., Canada, U.K., Australia), central, high prestige, strong bandwidth connections 3. Low prestige, peripheral, less bandwidth connections 4. English, not French, peripheral, no Ph.D.s, strong bandwidth connections 5. Continental Europe, not English 6. Chinese, less bandwidth connections 7. French, not English, peripheral, lower prestige 8. English, primarily (Jesuit Institutions), peripheral, low prestige 9. English, peripheral 10. Japanese & other Asian, peripheral, little bandwidth connections
  103. 103. Results - National • N = 58 Countries • Density = .924 • United States most central, followed by Germany, U.K., Canada – >30% of links ; >4 million outward & 1.9 million inward – Eigenvector centrality 10 times > Germany • Gini = .672, a core = periphery structure – U.S. (359), Germany (67), U.K. (67) & Canada (38) 53.1% of the universities – These four nations account for 68.3% of the links – Links distributed by power law; concentrated in a few countries • Cluster Analysis – 1 group of countries centered about U.S. & U.K.
  104. 104. Results – Predicting the Structure of the University URL-citation Network • Physical Distance Between Campuses – QAP Correlation = .005 No relationship between physical distance and web-citations • Same Country – – – – QAP Correlation = .065 Links 78.4% domestic; 21.6% international No Links 6.1% domestic; 93.9% international Mean Link Strength 1,415 with domestic; 42.5 international • Web-citations tend to be domestic
  105. 105. Results – Predicting University Centrality in Network --Correlations
  106. 106. Results – Predicting University Centrality in Network -- Regression In-degree R2 F P Size (log) English Bandwidth Rating Out-Degree .350 47.94 .000 ß .279 -.025 .268 .465 Betweenness .489 85.16 .000 t 6.49 -.516 5.70 10.53 all p< .001, except English for In-degree ß .123 .356 .302 .323 t 3.22 8.50 7.31 8.25 Eigenvector .579 122.25 .000 ß .282 .185 .336 .502 t 8.13 4.86 8.94 14.12 .310 39.94 .000 ß .150 .214 .208 .348 t 3.36 4.40 4.33 7.65
  107. 107. Results – Predicting the Structure of the URL-citation Network-National Level • QAP Correlations with National Level Network – Co-Authorships .772 – Citations .967 – Hyperlinks .545 – Student Flows .270 – Missing Data N = 52 on all except Student Flows, N = 48
  108. 108. Results – Predicting Nation’s Centrality in Network --Correlations
  109. 109. Results – Predicting National Centrality in the Network -- Regression In-degree .524 33.78 .000 35.12 .670 ß R2 F P Out-Degree ß t Nobles English Population .482 .4.80 GDP/capital .722 7.19 GDP .000 t .184 2.27 .398 4.70 .797 9.28 All relations are significant p < .02 Betweenness 22.99 ß .505 .000 t .443 4.33 .720 7.03 Eigenvector .642 31.05 .000 ß t .553 5.07 .183 2.15 .258 2.41
  110. 110. Discussion • So where is academic knowledge produced? – Primarily at prestigious English speaking institutions in the U.S.A. & U.K. , but also in Canada & Germany • Distance is unrelated to dissemination & collaboration via the Internet • Universities tend to link to others from the same country • Ten clusters- One composed of most prestigious institutions, suggesting exchanges of knowledge among this group • Centrality predicted by university size, its prestige (whether it offered doctoral degrees, its U.S. News ranking, the number of its faculty’s Noble Prizes), language of instruction (English), & national international bandwidth capacity
  111. 111. Discussion • At the national level, the countries formed a single group centered about the U.S. & the U.K. • U.S. is the most central, followed by Germany, U.K. & Canada – They accounted for the majority of the universities in the network • The International Network has a core-periphery structure with a few countries accounting for the majority of the links • International co-authorships, citations, student exchanges & the number of links among the individual countries are strongly predictive of the network’s structure • Centrality is predicted, by a country’s population & GDP, depending on the measure, it may also be predicted by language of instruction (English) & the number of Noble Prizes
  112. 112. Discussion • Results are consistent with Seeber, et al. (2012) – European university hyperlink network displays a center-periphery structure – centrality a function of the universities’ reputation – This study extends their conclusions to the global academic community
  113. 113. Discussion • Consistent with Ortega & Aguilla (2009) – “The world-class university network graph is comprised of national sub-networks that merge in a central core where the principal universities of each country pull their networks toward international link relationships. This network rests on the United States, which dominates the world network in conjunction with the aggregation of the European ones, especially the British and the German subnetworks. This situation may be caused mainly by the technological development of these countries and the production of international content, that is, English web pages. This second reason might explain the apparent backward situation of some East Asian countries.“ • World Systems Theory – Telephone (Barnett, 2001, 2012) – Internet (Barnett & Park, 2005, 2012; Park, Barnett & Chung, 2011) – Student flows (Barnett & Wu, 1995; Chen & Barnett, 2000; Jiang, 2013) – Patents, trademarks and copyrights (Nam & Barnett, 2011).
  114. 114. Discussion • Global academic community as a self-organizing system – Academic network may be considered an autopoietic or selfreplicated system – Evolved from traditional scientific activities (co-authorship, citing the research of others & other behaviors that required the sharing of information among scholars) – Krippendorf defines an autopoietic system as “a network of processes that produces all the components necessary to embody the very process that produces it”. The network recursively produces its components through the interaction in this historical reproductive network of postings on university websites & links among institutions
  115. 115. Discussion • There are environmental constraints that limit the possible states into which this system may evolve • issues of information property • policies of individual universities & national governments • scientific funding agencies (U.S. National Science Foundation) • Academic networks co-evolved with other global institutions • Universally, higher education is developing common curricula especially in the sciences (Lechner & Boli, 2005). This seems to be reflected in pattern of universities’ hyperlinks and web-citations
  116. 116. Thank you! See: Barnett, G.A. , Park, H.W., Jiang, K, Tang, C, & Aguillo, I.F., (2013), “A multi-level network analysis of web-citations among the world’s universities”, Scientometrics, DOI 10.1007/s11192-013-1070-0
  117. 117. Virtual Knowledge Studio (VKS) “Webometrics Studies” Revisited in the Age of “Big Data” Asso. Prof. Dr. Han Woo PARK CyberEmotions Research Institute Dept. of Media & Communication YeungNam University 214-1 Dae-dong, Gyeongsan-si, Gyeongsangbuk-do 712-749 Republic of Korea www.hanpark.net cerc.yu.ac.kr eastasia.yu.ac.kr asia-triplehelix.org
  118. 118. Big data  The term “big data” refers to “analytical technologies that have existed for years but can now be applied faster, on a greater scale and are accessible to more users. (Miller, 2013).  Big data sizes may vary per discipline.  Characteristics: Garner’s 3Vs plus SAS’s VC and IBM’s Veracity - Volume (amount of data), Velocity (speed of data in and out), Variety (range of data types and sources) - Variability: Data flows can be highly inconsistent with daily, seasonal, and event-triggered peak data loads - Complexity: Multiple data sources requiring cleaning, linking, and matching the data across system - Veracity: 1 in 3 business leaders don’t trust the information they use to make decisions. http://en.wikipedia.org/wiki/Big_data http://www-01.ibm.com/software/data/bigdata/
  119. 119. http://www.emc.com/leadership/digitaluniverse/iview/executive-summary-a-universe-of.htm
  120. 120. http://www.emc.com/leadership/digitaluniverse/iview/images/impact-ofconsumers-lg.jpg
  121. 121. Data-driven Research that focuses on extracting meaningful data from technosocio-economic systems to discover some hidden patterns. Today’s “big” is probably tomorrow’s “medium” and next week’s “small” and thus the most effective definition of “big data” may be derived when the size of data itself becomes part of the research problem. Loukides (2012)
  122. 122. Introduction  Webometrics is broadly defined as the study of webbased content (e.g., text, images, audio-visual objects, and hyperlinks) with primarily quantitative indicators for social science research goals and visualization techniques derived from information science and social network analysis.
  123. 123. • Han Woo Park - “hidden” and “relational” data about lots of people as well as the few individuals, or small groups • Lev Manovich - “surface” data about lots of people (i.e., statistical, mathematical or computational techniques for analyzing data) - “deep” data about the few individuals or small groups (i.e., hermeneutics, participant observation, thick description, semiotics, and close reading) 7
  124. 124. First type of Webometrics • Hyperlink Network Analysis - Inter-linkage: who linked to whom matrix - Co-inlink : a link to two different nodes from a third node - Co-outlink : A link from two different nodes to a third node Björneborn (2003)
  125. 125. Inter-link network analysis diagram among Korean escience sites within public domain WCU WEBOMETRICS INSTITUTE Mapping the e-science landscape In South Korea using the Webometrics method
  126. 126. Co-inlink network analysis WCU WEBOMETRICS INSTITUTE Mapping the e-science landscape In South Korea using the Webometrics method
  127. 127. Findings As seen in Figure 4, the network structure shows a clear butterfly pattern. There is one hub (ghism) that belongs to Park Gyun-Hye (Park GH, www.cyworld.com/ghism), the daughter of ex-president Park Jeong-Hee and one of two major GNP candidates (along with president-elect Lee MB) in the 2007 presidential race. Figure 4: Cyworld Mini-hompies of Korean legislators How do social scientists use link data from search engines to understand Internet-based political and electoral communication? WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Case 2. Cyworld Mini-hompies of Korean Legislators
  128. 128. Sociology of Hyperlink Networks of Web 1.0, Web 2.0, and Twitter A Case Study of South Korea
  129. 129. Introduction ‣ Online & offline lives ➭ co-constructing (e.g. Beer & Burrows, 2007) ‣ Politicians communicate with their constituencies using different platforms ‣ Questions: - What are the structural similarities and/or differences in South Korean politicians’ networks from Web 1.0 to Web 2.0 (and Twitter)? - Are online structures similar to structures in the physical world? - Are online patterns affected by offline relationships? ‣ Related studies conducted: - online social network analysis - online networks in Web 2.0 - role of Twitter on online politics
  130. 130. 2001 2000 ‣ 59 isolated in 2000 ‣ more centralised in 2001 ‣ network of 2001 ➭ a ‘star’ network - might affected by political events ➭ presidential election in 2001 Web 1.0
  131. 131. 2005 2006 ‣hubs disappearing ‣easy use of blogs ‣Clear boundaries between different parties ‣strong presence of GNP Assembly members ➭ party policy on using blogs Web 2.0
  132. 132. Politician Twitter Network (Following and Mention Network)
  133. 133. Conclusion Politicians Twitter Following-follower Network Politicians Twitter Mention Network
  134. 134. Bi-linked network of politically active A-list Korean citizen blogs (July 2005) URI=Centre DLP=Left GNP=Right Just A-list blogs exchanging links with politicians
  135. 135. Affiliation network diagram using pages linked to Lee’s and Park’s sites N = 901 (Lee: 215, Park: 692, Shared: 6)
  136. 136. Tweets on the name of S. Korea president 20
  137. 137. Viewertariat Networks: A Study of the 2012 South Korean Presidential Debate Park’s network Moon’s network
  138. 138. Reply-To Networks of Park’s & Moon’s Facebook page visitors during TV debates
  139. 139. “Those studies perpetuate the idea that linking behaviour is not random, and that links are ‘socially significant in some way’. In this perspective, links have an ‘information side-effect’, they can be used to understand other facts even though they were not individually designed to do so: ‘information side-effects are by-products of data intended for one use which can be mined in order to understand some tangential, and possibly larger scale, phenomena’
  140. 140. Park and his colleagues were extensively cited: 9 times! • • • • • • • • • Barnett GA, Chung CJ and Park HW (2011) Uncovering transnational hyperlink patterns and web mediated contents: a new approach based on cracking.com domain. Social Science Computer Review 29(3): 369–384. Hsu C and Park HW (2011) Sociology of hyperlink networks of Web 1.0, Web 2.0, and Twitter: a case study of South Korea. Social Science Computer Review 29(3): 354–368. Park HW (2003) Hyperlink network analysis: a new method for the study of social structure on the web. Connections 25(1): 49–61. Park HW (2010) Mapping the e-science landscape in South Korea using the webometrics method. Journal of Computer-Mediated Communication 15(2): 211–229. Park HW and Jankowski NW (2008) A hyperlink network analysis of citizen blogs in South Korean politics. Javnost: The Public 15(2): 5–16. Park HW and Thelwall M (2003) Hyperlink analyses of the World Wide Web: a review. Journal of Computer-Mediated Communication 8(4). Park HW and Thelwall M (2008) Developing network indicators for ideological landscapes from the political blogosphere in South Korea. Journal of ComputerMediated Communication 13(4): 856–879. Park HW, Kim C and Barnett GA (2004) Socio-communicational structure among political actors on the web in South Korea. New Media & Society 6(3): 403–423. Park HW, Thelwall M and Kluver R (2005) Political hyperlinking in South Korea: technical indicators of ideology and content. Sociological Research Online 12(3).
  141. 141. A comment from those who are NOT doing a hyperlink analysis • In a chapter of The Sage Handbook of Online Research Methods edited by Fielding et al. (2008), Horgan emphasizes that ‘link analysis’ has become an active research domain in examining social behavior online. 25
  142. 142. A threat to Webometrics • The key application in this area is to collect some incoming, outgoing, inter-linking, and co-linking data from search engines - AltaVista in early 2000 - Yahoo renewed the AltaVista’s hyperlink commands via “Site Explorer” and its API - Yahoo discontinued its API option for interlinkage data in April 2011, and finally stopped its popular Site Explore service in November 2011
  143. 143. http://cybermetrics.wlv.ac.uk/Que riesForWebometrics.htm
  144. 144. A new proposal • Mike Thelwall - URL citation searches with the Bing search API facilities • Liwen Vaughan - Incoming hyperlinks from Alexa.com Can these "alternative" techniques be acceptable for scientific publishing?
  145. 145. A new proposal : SEO Tools • - Search Engine Optimization Tools http://www.majesticseo.com/ http://www.opensiteexplorer.org/ https://ahrefs.com/ Enrique Orduña-Malea & John J. Regazzi (2013). Influence of the academic Library on U.S. university reputation: a webometric approach. Technologies. 1, 26-43, http://www.mdpi.com/2227-7080/1/2/26
  146. 146. Webometrics Ranking of World Universities The link visibility data is collected from the two most important providers of this information: Majestic SEO and ahrefs. Both use their own crawlers, generating different databases that should be used jointly for filling gaps or correcting mistakes. The indicator is the product of square root of the number of backlinks and the number of domains originating those backlinks, so it is not only important the link popularity but even more the link diversity. The maximum of the normalized results is the impact indicator. http://www.webometrics.info/en/Methodology
  147. 147. Interlinkage among world universities • Barnett, G.A., Park, H. W., Jiang, K., Tang, C., & Aguillo, I. F. (2013 forthcoming). A MultiLevel Network Analysis of Web-Citations Among The World’s Universities. Scientometrics*. Isidro F. Aguillo “Large interlinking matrix (1000*1000) are no longer possible to obtain. Perhaps national academic systems (200 or 300 institutions)”
  148. 148. Intentional inattention among Information Scientists? • Robert Ackland (2013). Web Social Science. - http://voson.anu.edu.au/ • Richard Rogers (2013). Digital Methods. - https://www.issuecrawler.net/index.php - https://www.digitalmethods.net/Dmi/Tool Database
  149. 149. Let us move to Web Visibility Analysis Frequently occurring key words in e-science webpages in Korea Created on Many Eyes(http://many-eyes.com) Words are larger according to the frequency of their occurrence but their positions are randomly-chosen for the best visualization WCU WEBOMETRICS INSTITUTE
  150. 150. Websites retrieved more than two times Note: Websites are larger according to their frequency of retrieval; however, heir colors and locations are randomly-chosen for the best visualization WCU WEBOMETRICS INSTITUTE
  151. 151. 2nd type of Webometrics: Web Visibility  Web visibility as an indicator of online political power   Presence or appearance of actors or issues being discussed by the public (Internet users) on the web. Tracking web visibility is powerful way to get an insight into public reactions to actors or issues.  Recent studies indicates the positive relationships between politicians’ web visibility level and election.  Also, the co-occurrence web visibility between two politicians represents their hidden online political relationships based on the public perception.
  152. 152. Results – Web Visibility (co-occurrence)
  153. 153. Results – Correlation & Path Analysis Correlation 1 (N=278) 1 Finance 2 (N=278) 3 (N=234) 1 0.420** 0.101 1 0.184** 2 Web 3 Vot e 1 Spearman Correlation 1 (N=278) 1 Finance 2 (N=278) 3 (N=234) 1 0.513** 0.090 1 0.163* 2 Web Political finance’s indirect effect = .076 3 Vot e Note. * p<.05, ** p<.01 ** p<.01 1
  154. 154. Results – QAP Correlation 1 1 Committee 2 Constituency 2 3 1 0.004 -0.016 1 3 Party 4 Gender 5 Age 6 Incumbent 7 Web 8 Finance Note. * p<.05, ** p<.01 4 0.025 0.097** -0.007 1 0.027 1 5 -0.021 6 7 8 -0.074** 0.045** -0.037** -0.043** -0.064** 0.105** -0.119** -0.045* -0.050* 0.242** -0.094** 0.024 0.031 1 0.179** -0.051* 0.049* 1 0.098** 0.041 -0.060** 1 -0.224** -0.158** 1
  155. 155. e-리서치 도구의 활용: 웹가시성 분석  블로그 공간에서 후보자들의 웹가시성 수준과 득표 수간 에 밀접한 상관성을 나타냄. (임연수, 박한우, 2010, JKDAS) 실제 득표수 29,120 평균 블로그 수 19,427 14,218 3,071 2,125 504 경대수 정범구 정원헌 박기수 이태희 김경회
  156. 156. 2009년 10월 28일 재보선 결과 - 당선자 모두 블로그 가시성 높음
  157. 157. I. 소셜 미디어의 특징 및 영향력 10.26 재보궐 선거 사례 • (2) 페이스북에서 이름이 동시에 언급되는 이름 연결망을 구성 하여 분석 • 초반에는 두 후보자가 비슷하게 언급되다가, 중반에 접어들자 박원순 지지자들과 박원순이 언급되면서 나경원 후보자 지지자가 안보이게 되고, 종반에는 박원순 중심으로 네트워크가 재편되며 종결됨
  158. 158. I. Semantic network에서 중심성 비교 10.26 재보궐 선거 사례 (2) • 서울시장 선거 관련 메세지들의 내용 을 분석하여 나오는 단어들의 빈도 분석 • 초반부터 나경원 후보는 빈도가 떨어 지다가, 후반에 박원순 후보와 경쟁 및 선거 결과를 이야기하면서 나타나 는 경우를 제외하고는 줄곳 담론외곽 에 존재 • 안철수 효과는 초반에 크고, 중반이 후 떨이지는 효과가 나타났으나, 한 나라당이라는 언급이 높게 나오면서 집권여당에 반하는 정서가 나타나, 선거의 성격을 말해줌
  159. 159.   As Lim & Park (2011, 2013) claim, the use of web mentions of politicians’ names is particularly useful for hierarchically ranking individual politicians. However, it may not sufficiently capture the entropy probability of an event (hidden in changing communication structures) resulting from the amount of information conveyed by the occurrence of that event (Shannon, 1948).
  160. 160.  Taleb (2012) argues that society can be conceived as a complex fabric consisting of the extended disorder family including uncertainty, chance, entropy, etc.  Therefore, such disorder system can be better derived from empirical data mining, not obtained by a priori theorem.  Uncertainty exists when three or more events take place simultaneously and is increasingly beyond the control of individual events (Leydesdorff, 2008).
  161. 161.  In social and communication sciences, entropy-based indicators have been widely used for exploring entropy values generated from university-industrygovernment (UIG) relationships.  This “Triple Helix Model” (THM) can be applied to the concurrence of a pair of two or three terms in the public search engine database
  162. 162. Mapping Election Campaigns Through Negative Entropy: Triple and Quadruple Helix Approach to Korea’s 2012 Presidential Election Social media platforms have become a notable venue for Korean voters wishing to share their opinions and predictions with others (Park et al., 2011; Sams & Park, 2013).  Politicians have made increasingly use of SNSs to provide updates and communicate with citizens (Hsu & Park, 2012).  With the increasing proliferation of smartphones and portable computers in Korea, SNSs have been widely used for facilitating political discourse.  Prior studies have found that Web 1.0 contents tended to contain the more enduring political and electoral statements of the public in various contexts. 
  163. 163. Introduction  To better understand the dynamics of the 2012 presidential election in Korea, this study estimates the web visibility of the three major candidates— Geun-Hye Park (PARK), Cheol-Soo Ahn (AHN), and Jae-In Moon (MOON)—in the entire digital sphere.
  164. 164. Literature Review The total probabilistic entropy (uncertainty) produced by changes in one or two dimensions is always positive, which is in accordance with the second law of thermodynamics (Theil, 1972, p. 59).  On the other hand, the relative contribution of each event to the summation in three or four dimensions can be positive, zero, or negative (configurational information).  This configurational information provides a measure of synergy within a complex communication system. Network effects occur in a systemic and nonlinear manner when loops in the configuration generate redundancies in relationships between three or four events (Leydesdorff, 2008). 
  165. 165. Method: Data collection     The number of hits for each search query per media channel (Facebook, Twitter, and Google) was harvested. The hit counts obtained from Google.com were employed to look primarily at entropies represented on a set of digitally accessible documents (e.g., online versions of newspapers, online word-of-mouth, Web 1.0 contents, etc.). We measured the occurrence and co-occurrence of the politicians’ names based on their bilateral, trilateral, and quadruple relationships by using Boolean operators. For example, we measured the number of web and social media mentions referring only to PARK (this is, no mention of AHN, MOON, or the term “president”).
  166. 166. SNS 미디어에 따른 중심성에 따른 시각화
  167. 167. Literature Review Twitter can be very effective to amplify messages particularly in terms of their one-to-many mode of communication (Barash & Golder, 2010).  Twitter is viable both as a political news and communication channel (González-Bailón, Borge-Holthoefer, Rivero & Moreno, 2011; Hsu & Park, 2011, 2012; Otterbacher, Shapiro, & Hemphill, 2013)  and to citizens who look for platforms for political participation and engagement (Hsu, Park, & Park, 2013; Kim & Park, 2011; Tufekci& Wilson, 2012). 
  168. 168. Literature Review    The mode of information sharing on Facebook differs from that on Twitter. Facebook functions as a living room where friends talk to one another. Facebook can be a mixture of interpersonal and mass channels for the sharing of informational as well as social messages in a context of political campaign (Bond et al., 2012; Effing, van Hillegersberg, & Huibers, 2011; Robertson, Vatrapu, & Medina, 2010; Vitak et al., 2011). Both Twitter and Facebook communications seem to be biased because two platforms have been particularly dominated by the “2040 Generation”, who are generally categorized as political liberals in Korea (Kwak et al., 2011).
  169. 169. Research questions  Therefore, it is important to examine what (social) media conversations are more likely to generate more entropies that others and which politician:  RQ 1) What (social) media generate (negative) entropy more than others across different periods?  RQ 2) Which politician (or which pair of politicians) generates entropy more than others for bilateral, trilateral, or quadruple relationships across various media and periods?
  170. 170. Method: Measuring (negative) entropy  Figure 1. Binary Entropy Plot
  171. 171.  Entropy values (expressed as T for transmission) for bilateral relationships are, by definition, positive. Here T is defined as the difference in uncertainty when the probability distributions of two incidents (e.g., i and j) are combined. The mutual information transmission capacity, expressed in T values, is measured by “bits” of information (for a more detailed mathematical definition, see Leydesdorff, 2003):  Hi = – Σi pi log2 (pi); Hij = – Σi Σj pij log2 (pij), Hij = Hi + Hj – Tij , Tij = Hi + Hj – Hij (1) Here Tij is zero if the two distributions are mutually independent and positive otherwise (Theil, 1972).   
  172. 172.  On the other hand, T values for trilateral and quadruple relationships can be negative, positive, or zero depending on the size of contributing terms. Therefore, it is necessary to compare the absolute value of each (negative) entropy value when entropy values are calculated for trilateral and quadruple relationships. In the case of entropy values for trilateral and quadruple relationships, the higher the absolute entropy value, the more balanced the communication system is. Let p denote PARK; a, AHN; and m, MOON and formulate mutual information in these three dimensions as follows (Abramson. 1963, p. 129):  Tpam = Hp + Ha + Hm – Hpa – Hpm – Ham + Hpam  Here we are interested not only in information on mutual relationships between these three candidates but also in semantic relationships with respect to the term “president.” Accordingly, we measure the entropy value by using mutual information in these four dimensions (here “r” denotes “president”):  Tpamr = Hp + Ha + Hm + Hr – Hpa – Hpm – Hpr – Ham – Har – Hmr + Hpam + Hpar + Hpmr + Hamr –Hpamr (3) (2)
  173. 173. Results  Figure 2. Entropy Values Across Media Channels and Time Periods
  174. 174. Results  Figure 3. T Values for Bilateral and Trilateral Relationships on November 3.
  175. 175. Results  Figure 4. T Values for Bilateral Relationships between Park and Moon
  176. 176. Discussion and conclusions    Twitter has scored the most negative entropy values and Facebook followed. Google came last. This indicates that Twitter is the most open communication system. The entropy values for liberal candidates (AHN and MOON) have been higher than their conservative opponent PARK on social media than Google sphere. This may not be surprising because both Twitter and Facebook have particularly appeared to the Korean citizens in the age of late teenagers to early 40s.
  177. 177. Discussion and conclusions PARK’s entropy has been slightly higher on Google than her liberal challenger MOON.  Park was successful in garnering a strong support from senior voters in their 50s and 60s accounted for 39% of the population, up from 29% a decade ago (Wall Street Journal, 2012).  Exit poll also revealed that PARK gained a support from 62% of voters in their 50s and 72% of voters in their 60s. Indeed, the most significant statistic on the election was that South Koreans in their 20s, 30s, and 40s actually voted 65.2%, 72.5%, and 78.7% respectively but 89.9% in 50s and 78.8% over 60s went to the polling booth. 
  178. 178. Paper-code Keynote Speech “Creativity and TRIZ”for the Knowledge Network Analysis in the Emerging Big Data Research” - DISC 2013 2013. 12. 14. Dr. Jae Ho Par, Ph.D. Managing Director of GRCIOP Professor Emeritus Jae Ho Park Yeungnam University
  179. 179. Curriculum Vitae Paper-code December 14, 2013 Professor emeritus Jae H. Park, Ph.D - Professor Emeritus , Industrial and Organizational Psychology, Yeungnam University, South Korea -Chairman, Global TRIZ Conference, Organizing Committes - Chairman, Korean Society of Creativity - Managing Director, GRCIOP Research Center - Senior Advisor, ICEDR(International Consortium for Executive Development Research, Boston, USA - Ph.D., Organizational Psychology, Goettingen University, Germany - MA, Social Psychology, Seoul National University - BA, Seoul National University <Academic Career> -  Harvard University, Research Professor. USA  University of Michigan, Exchange Professor, Ann Arbor, Michigan, USA  Yokohama National University, Research Fellow Professor, Japan  CSPP(California School of Professional Psychology), Teaching Professor, 1999-2000  Senior Advisor, ICEDR(International Consortium for Executive Development Research), USA  Visiting Professor, Meio University, Japan, current Partner, THT Cross-cultural Consulting, Amsterdam, the Netherlands  Partner, SYMLOG Consulting Group, San Diego, USA  Liscencee, Center for Creative Leadership(CCL), Greensboro, USA,  Partner, Global Integration, UK
  180. 180. Paper-code <International Consulting and Training>  Samsung Electronics; Creativity and Innovation “Change Begins with Me” Samsung New Management, Train the trainers for 6,000 managers.  JMA(Japan Management Association and FMIC(Future Management and Innovation Consulting, Japan ), SYMLOG Diagnosis, Team-building and Coaching, Tokyo, Japan - LG Philips Displays, M & A Process Consultation, Coaching, Diagnosis  LG Electronics, DAC(White electronics Division), Changwon, Korea  Hyundai Motor Company, Creativity and Innovation Program, Korea  Samsung Electronics, Large Scale Change, Korea  BorgWarner, Detroit, USA  Ericsson, Sweden  Applied Materials Korea, Coaching and Consultation, Seoul, Korea  Goldman Sachs, Integration Project Coaching, with THT Consulting Group, 2007  MetLife, Coaching for Asset Managers, 2007  Mirae Assets Stock Company, Creativity Coaching, 2010  Team-building and Innovation, Trondheim University, Norway
  181. 181. Paper-code <International Network>  Center for Creative Leadership, Partner, Liscencee, North Carolina, USA  SPGR Consulting, Oslo, Norway  JMAC(Japan Management Association Consulting) Tokyo, Japan  SYMLOG Consulting Group, Researcher and Partner, San Diego, USA  Global Integration, Partner, London, United Kingdom  Japan Creativity Research Center, Partner, Tokyo, Japan  THT Cross-cultural Consulting(Trompenaars & Turner), Amsterdam, Partner, the Netherlands  ICEDR(International Consortium for Executive Development Research) Boston, USA <Consultant and Advisor >  Samsung HRD Center  Samsung Electronics  Samsung SDI  LG Education Center  LG Electronics  POSCO HRD Center <Contact> Phone; 82-53-810-2230(Office) Fax; 82-53-810-4610 Mobile; 82-10-8751-7579 email; grciop@gmail.com
  182. 182. TRIZ Founder G. S. Altshuller (1926~1998) Father of TRIZ Global TRIZ Conference 2013 | www.koreatrizcon.kr Seoul Trade Exhibition & Convention, Seoul, Korea | July 09-11, 2013
  183. 183. Paper-code What is TRIZ ? TRI Z is a tool for Thinking but not instead of thinking G. Altshuller
  184. 184. Change of major discipline Paper-code
  185. 185. Paper-code From Tools to Subjects  Labor : Human Robot Creativity
  186. 186. Paper-code TRIZ 6 Sigma CAE
  187. 187. Innovation in Global companies Paper-code
  188. 188. Paper-code 1. 2. 3. 4. 5. 6. 7. Toyota Method QFD TOC TRIZ 6 Sigma Taguchi Method 7 Tools of Product Design
  189. 189. Paper-code
  190. 190. Paper-code  Research Areas ◦ Understanding creative cognition and computation ◦ Creativity to stimulate breakthrough in science and engineering ◦ Educational approaches that encourage creativity ◦ Supporting creativity with IT
  191. 191. Paper-code  INSA     Strasbourg http://www.insa-strasbourg.fr/en/news/news.html Advanced Master of Innovative Design 5 Semesters for Intensive TRIZ Since 9 years in operation
  192. 192. Paper-code 2008. 11. 28
  193. 193. Edison and Altshuller • • • • • Everybody can be a Inventor TRIZ Diffusion; No cost Developed TRIZ in Prison Benevolent Mentor (Dialectics; ideal Communist) Paper-code
  194. 194. Paper-code  TRIZ  Analyzed many Patents  By Creative Problem Solving Methods  Inductive Research Methods
  195. 195. Paper-code
  196. 196. Paper-code Various views on TRIZ • • • • • • • From Knowledge Management From 6 Sigma From Engineering Design From Innovation From Creativity From R&D Etc…
  197. 197. Paper-code
  198. 198. Paper-code TRIZ as a Science Technical Systems Social Systems Natural Systems TRIZ N&A Narbut, 2003
  199. 199. Paper-code 5 Levels of Invention ① Apparent Solution (32%) ① - Simple ② Simple Improvement within current system (45%) ③ Major improvement (18%) - within same science ④ Innovation within current system (4%) - Application different science principle ⑤ Pioneer Invention (1%) - New principle and Paradigm Shift ⑤ ④ ③ ②
  200. 200. Paper-code Effects in TRIZ Effects Systematized Information funds Trends Su-F Development Models ARIZ, Standards N&A Narbut, 2003
  201. 201. Paper-code Common Approach TRIZ Innovation involves the creation of new ideas Innovation involves adapting existing ideas Trained in the notion of the ‘great idea’. Popular mythology - “Einstein” as model. Belief that ‘six months in the lab beats one hour spent in the library’. Tap existing solutions. Look outside of discipline and to Nature. Key benefit: reduces perceived risk of innovation (predictable, higher chance of success).
  202. 202. Paper-code Korea; Creative Economy via Creativity : Expansion & Convergence Pie Bibimbap - 2/10 -
  203. 203. Creativity and TRIZ Paper-code * Korea Academic TRIZ Association Industry-Academia Knowledge sharing Contributor for industry competitiveness and creative talent by TRIZ Founded in May 2010  Participating of Univ. & Co.  Homepage: www.katatriz.or.kr 32 Co. 29 Univ. - 3/10 -
  204. 204. Paper-code Main Activities Expanded use of TRIZ and social contribution Evolution Nurturing creative talent MATRIZ & KATA MOU Problem-solving, Patent-creation Biz. TRIZ research Univ. professor Workshop Anti-school violence program TRIZ education Charity fair TRIZ Youth Acamedy Lectures for SMEs Consulting for SMEs problem-solving Technical TRIZ application 2010 2011 2012 2013 Time - 5/10 -
  205. 205. TRIZ Activities in Korea Paper-code Company : Development of Innovative Products, Problem-Solving and Patents Creation  Core tech & innovative product  Foundation of TRIZ Univ.  TRIZ Elite  Development of POSCO methodology  TRIZ research group  Internal TRIZ Conference  Mixing DFSS & TRIZ  Strategic R&D patent creation  Patent creation  On-site TRIZ process designed to  TRIZ research group improve on-site work performance - 6/10 -
  206. 206. TRIZ Activities in Korea Paper-code University : Utilizing TRIZ in subject of “Creative design” POSTECH  Master course curriculum  TRIZ Project organization YONSEI  Creative engineering education  Inter-discipline activities and courses  Engineering certification program HANYANG  Creative design education  Business management and creative design curriculum POLYTECHNIC  Mechanical engineering-focused courses  KOREA/RUSSIA cooperation center ※ TRIZ application supported by the government and research institutions (i.e. Ministry of Trade, Industry and Energy and ETRI) - 7/10 -
  207. 207. Paper-code    Systematic innovator Learn and practice by yourself. Participate as a member of TRIZ Association(Daegu-Gyungbuk Regional Association): via Band
  208. 208. Paper-code Recognition that  (technical) systems evolve  Towards the increase of ideality  By overcoming Contradiction  Mostly with minimal introduction of (free) Resources Thus, for creative problem solving  TRIZ provides a dialectic ways of thinking, i.e.,  To understand the problem as a system  To image the Ideal solution first  And solve Contradiction
  209. 209. Paper-code GRCIOP Global Network ICEDR(International Consortium for Executive Development Research(USA) Global Integration(United Kingdom) SYMLOG Consulting Group(USA) Center for Creative Leadership(USA) THT Consulting(the Netherlands) Endre Sjovold Association(Norway)
  210. 210. The Geopolitics of New Media RANDY KLUVER TEXAS A&M UNIVERSITY
  211. 211. The context  The rise of “new media” has transformed politics, economics, and societies.  But, “Internet Studies” as a field ignores the geopolitical issues associated with the rise of new media technologies   Lots of emphasis on “politics” and the internet, but little on the relations between states “Arab Spring”-events occur, but the focus remains primarily on a domestic context  Likewise, traditional IR theory focuses primarily on elite level strategy, and doesn’t have the tools to account for publics
  212. 212. The Big Picture
  213. 213. Issue 1: The implications of a “networked” globe on geopolitics  Shifting configurations of influence  Networked, rather than hierarchical  Highly transnational  “foreign” vs “domestic” doesn’t capture the reality  The conversation has become global, especially among elites    Values Politics Economics  But, influence depends on your connectedness to the global conversation  Thus, dependent on access to technological infrastructure
  214. 214. Example: Influential players in discourse surrounding the Egyptian coup weren’t Egyptian!
  215. 215. Saudi #2
  216. 216. But where was the Muslim Brotherhood?
  217. 217. Constraints on global networks  Language  Technological diffusion  Domestic politics/economic priorities  Platforms/applications
  218. 218. Should networks follow language groups?
  219. 219. English as the dominant carrier of global conversation
  220. 220. Internet languages
  221. 221. A new bi-polar world?
  222. 222. Peer to Peer Diplomacy: Global Social Network Usage
  223. 223. Twitter’s global web traffic (not counting sms, im, etc)
  224. 224. P2PD: China’s exclusion from “facebook friendships”
  225. 225. South Korea’s facebook friendships
  226. 226. Russia’s Facebook friendships
  227. 227. Iran’s Facebook friendships
  228. 228. Public Diplomacy: Twitter targets
  229. 229. China’s Twitter outreach
  230. 230. Russia’s Twitter Outreach
  231. 231. Public Diplomacy: E-diplomacy index
  232. 232. How is China doing?
  233. 233. South Korea’s E-Diplomacy
  234. 234. Issue 2: Information Access/Control  Crowd Sourced  Unprecedented access to sensitive information  Stratified  Customized “The spread of information networks is forming a new nervous system for our planet. When something happens in Haiti or Hunan, the rest of us learn about it in real time-from real people.” US Sec of StateHillary Clinton, 2010
  235. 235. Wikileaks: Crowd-sourced espionage or invaluable public service?  Revealed US war plans and operations, as well as diplomatic secrets  Led to multiple recriminations, including attempted assassination of Saudi ambassador  Snowden: hero or traitor?
  236. 236. The value of geographic knowledge
  237. 237. Need a drone?
  238. 238. Issue Three: Policies  Re-articulation of “national interest”  Alec J. Ross and “21st Century Statecraft”  “addresses new forces propelling change in international relations that are pervasive, disruptive, and difficult to predict.” US Dept of State  Perhaps what we can predict  Publics more important than elites  Don’t assume you can keep secrets  Companies comply with national laws more for reputational reasons than for fear of sanction
  239. 239. The Internet Freedom Agenda  “Countries that restrict free access to information or violate the basic rights of internet users risk walling themselves off from the progress of the next century.” Hillary Clinton, January 2010, Remarks on Internet Freedom  “Let’s be clear. This disclosure is not just an attack on America-it’s an attack on the international community.” Hillary Clinton, November 2010, after the Wikileaks release.  Conclusion: no set of easy answers
  240. 240. Final thoughts…..  We need far more sustained attention to the impact of new media in between states, as well as within states.  Unrealistic to simply say “NO,” no matter how loudly we say it. The technology won’t be unmade.  We are in uncharted, and largely unstudied, territory, and our policies are being driven by what is technically feasible, rather than what is desirable.
  241. 241. A project from the Social Media Research Foundation: http://www.smrfoundation.org
  242. 242. About Me Introductions Marc A. Smith Chief Social Scientist Connected Action Consulting Group Marc@connectedaction.net http://www.connectedaction.net http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://delicious.com/marc_smith/Paper http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.smrfoundation.org
  243. 243. Social Media Research Foundation http://smrfoundation.org
  244. 244. Social Media Research Foundation People Disciplines Institutions University Faculty Computer Science University of Maryland Students HCI, CSCW Oxford Internet Institute Industry Machine Learning Stanford University Independent Information Visualization Microsoft Research Researchers UI/UX Illinois Institute of Technology Developers Social Science/Sociology Connected Action Network Analysis Cornell Collective Action Morningside Analytics
  245. 245. What we are trying to do: Open Tools, Open Data, Open Scholarship • Build the “Firefox of GraphML” – open tools for collecting and visualizing social media data • Connect users to network analysis – make network charts as easy as making a pie chart • Connect researchers to social media data sources • Archive: Be the “Allen Very Large Telescope Array” for Social Media data – coordinate and aggregate the results of many user’s data collection and analysis • Create open access research papers & findings • Make “collections of connections” easy for users to manage
  246. 246. What we have done: Open Tools • NodeXL • Data providers (“spigots”) – – – – – – – – ThreadMill Message Board Exchange Enterprise Email Voson Hyperlink SharePoint Facebook Twitter YouTube Flickr
  247. 247. What we have done: Open Data • NodeXLGraphGallery.org – User generated collection of network graphs, datasets and annotations – Collective repository for the research community – Published collections of data from a range of social media data sources to help students and researchers connect with data of interest and relevance
  248. 248. What we have done: Open Scholarship
  249. 249. What we have done: Open Scholarship
  250. 250. Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections from people to people. 10
  251. 251. Patterns are left behind 11
  252. 252. There are many kinds of ties…. Send, Mention, Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in… http://www.flickr.com/photos/stevendepolo/3254238329
  253. 253. Social Network Theory http://en.wikipedia.org/wiki/Social_network • Central tenet – Social structure emerges from – the aggregate of relationships (ties) – among members of a population • Phenomena of interest – Emergence of cliques and clusters – from patterns of relationships – Centrality (core), periphery (isolates), – betweenness • Methods – Surveys, interviews, observations, log file analysis, computational analysis of matrices Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.716 (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
  254. 254. SNA 101 • Node A – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge B – Relationship connecting nodes; can be directional C • Cohesive Sub-Group – Well-connected group; clique; cluster • Key Metrics A B D E – Centrality (group or individual measure) D • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) • Measure at the individual node or group level E – Cohesion (group measure) • Ease with which a network can connect • Aggregate measure of shortest path between each node pair at network level reflects average distance – Density (group measure) • Robustness of the network • Number of connections that exist in the group out of 100% possible G F – Betweenness (individual measure) • # shortest paths between each node pair that a node is on • Measure at the individual node level • Node roles H I C – Peripheral – below average centrality – Central connector – above average centrality – Broker – above average betweenness E D
  255. 255. NodeXL Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graph theory as easy as a pie chart, with integrated analysis of social media sources. http://nodexl.codeplex.com
  256. 256. Now Available
  257. 257. Communities in Cyberspace
  258. 258. Goal: Make SNA easier • Existing Social Network Tools are challenging for many novice users • Tools like Excel are widely used • Leveraging a spreadsheet as a host for SNA lowers barriers to network data analysis and display
  259. 259. http://www.flickr.com/photos/badgopher/3264760070/
  260. 260. http://www.flickr.com/photos/druclimb/2212572259/in/photostream/
  261. 261. http://www.flickr.com/photos/hchalkley/47839243/
  262. 262. http://www.flickr.com/photos/rvwithtito/4236716778
  263. 263. http://www.flickr.com/photos/62693815@N03/6277208708/
  264. 264. Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.
  265. 265. NodeXL Network Overview Discovery and Exploration add-in for Excel 2007/2010 A minimal network can illustrate the ways different locations have different values for centrality and degree
  266. 266. Hubs
  267. 267. Bridges
  268. 268. http://www.flickr.com/photos/storm-crypt/3047698741
  269. 269. Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith. 2007. Visualizing the Signatures of Social Roles in Online Discussion Groups. The Journal of Social Structure. 8(2). Experts and “Answer People” Discussion people, Topic setters Discussion starters, Topic setters
  270. 270. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  271. 271. http://www.flickr.com/photos/amycgx/3119640267/
  272. 272. #teaparty 15 November 2011 #occupywallstreet 15 November 2011 http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
  273. 273. Like MSPaint™ for graphs. — the Community Introduction to NodeXL
  274. 274. NodeXL Ribbon in Excel
  275. 275. NodeXL data import sources
  276. 276. Example NodeXL data importer for Twitter
  277. 277. NodeXL imports “edges” from social media data sources
  278. 278. NodeXL displays subgraph images along with network metadata NodeXL creates a list of “vertices” from imported social media edges
  279. 279. NodeXL Automation makes analysis simple and fast Perform collections of common operations with a single click
  280. 280. NodeXL Generates Overall Network Metrics
  281. 281. 50
  282. 282. 51
  283. 283. 52
  284. 284. 53
  285. 285. 54
  286. 286. 55
  287. 287. 56
  288. 288. 57
  289. 289. 58
  290. 290. Divided Polarized Unified In-group Fragmented Brand Clustered Communities In-Hub & Spoke Broadcast Out-Hub & Spoke Support
  291. 291. 6 kinds of Twitter social media networks
  292. 292. #My2K Polarized
  293. 293. #CMgrChat In-group / Community
  294. 294. Lumia Brand / Public Topic
  295. 295. #FLOTUS Bazaar
  296. 296. New York Times Article Paul Krugman Broadcast: Audience + Communities
  297. 297. Dell Listens/Dellcares Support
  298. 298. SNA questions for social media: 1. 2. 3. 4. What does my topic network look like? What does the topic I aspire to be look like? What is the difference between #1 and #2? How does my map change as I intervene? What does #YourHashtag look like?
  299. 299. Twitter Network for “Microsoft Research” *BEFORE*
  300. 300. Twitter Network for “Microsoft Research” *AFTER*
  301. 301. Network Motif Simplification Cody Dunne, University of Maryland
  302. 302. Network Motif Simplification D-connector (glyph on the right) Fan(glyph on the right) D-clique (glyphs for 4, 5, and 6 member cliques below) Dr. Cody Dunne
  303. 303. NodeXL Graph Gallery
  304. 304. Scholars using NodeXL • Communications – Katy Pearce – Itai Himelboim • Business – Scott Dempwolf • Humanities/Classics – Diane Cline
  305. 305. C. Scott Dempwolf, PhD Research Assistant Professor & Director UMD - Morgan State Center for Economic Development
  306. 306. What is Social Network Analysis? How is it useful for the humanities? 1. New framework for analysis 2. Data visualization allows new perspectives – less linear, more comprehensive Social Network Analysis and Ancient History Diane H. Cline, Ph.D. University of Cincinnati
  307. 307. NodeXL calculates metrics about networks and content
  308. 308. The Content summary spreadsheet displays the most frequently used URLs, hashtags, and user names within the network as a whole and within each calculated sub-group.
  309. 309. NodeXL Graph Gallery 80
  310. 310. NodeXL as a Research Tool 81
  311. 311. NodeXL as a Teaching Tool I. Getting Started with Analyzing Social Media Networks 1. Introduction to Social Media and Social Networks 2. Social media: New Technologies of Collaboration 3. Social Network Analysis II. NodeXL Tutorial: Learning by Doing 4. Layout, Visual Design & Labeling 5. Calculating & Visualizing Network Metrics 6. Preparing Data & Filtering 7. Clustering &Grouping III Social Media Network Analysis Case Studies 8. Email 9. Threaded Networks 10. Twitter 11. Facebook 12. WWW 13. Flickr 14. YouTube 15. Wiki Networks http://www.elsevier.com/wps/find/bookdescription.cws_home/723354/description 82
  312. 312. What we want to do: (Build the tools to) map the social web • Move NodeXL to the web: (Node[NOT]XL) – Node for Google Doc Spreadsheets? – WebGL Canvas? D3.JS? Sigma.JS • Connect to more data sources of interest: – RDF, MediaWikis, Gmail, NYT, Citation Networks • Solve hard network manipulation UI problems: – Modal transform, Time series, Automated layouts • Grow and maintain archives of social media network data sets for research use. • Improve network science education: – Workshops on social media network analysis – Live lectures and presentations – Videos and training materials
  313. 313. NodeXL Results • Easy to learn, yet powerful and insightful • Widely used by both students and researchers • Free and open source sofware • World-wide team of collaborators Malik S, Smith A, Papadatos P, Li J, Dunne C, and Shneiderman B (2013), “TopicFlow: Visualizing topic alignment of Twitter data over time. In ASONAM '13. Bonsignore EM, Dunne C, Rotman D, Smith M, Capone T, Hansen DL and Shneiderman B (2009), "First steps to NetViz Nirvana: Evaluating social network analysis with NodeXL", In CSE '09. pp. 332-339. DOI:10.1109/CSE.2009.120 Mohammad S, Dunne C and Dorr B (2009), "Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus", In EMNLP '09. pp. 599-608. Smith M, Shneiderman B, Milic-Frayling N, Rodrigues EM, Barash V, Dunne C, Capone T, Perer A and Gleave E (2009), "Analyzing (social media) networks with NodeXL", In C&T '09. pp. 255-264. 84 DOI:0.1145/1556460.1556497
  314. 314. How you can help Sponsor a feature Sponsor workshops Sponsor a student Schedule training Sponsor the foundation Donate your money, code, computation, storage, bandwidth, data or employee’s time • Help promote the work of the Social Media Research Foundation • • • • • •
  315. 315. Available Now in NodeXL! • • • • • • • • • • • • • Motif Simplification Group-in-a-Box Layouts Data import spigots Excel functions & macros Network statistics Layout algorithms Filtering Clustering Attribute mapping Automate analyses Email reporting Graph Gallery C# libraries nodexl.codeplex.com
  316. 316. Strategies for social media engagement based on social media network analysis
  317. 317. A project from the Social Media Research Foundation: http://www.smrfoundation.org
  318. 318. International Collaboration & Green Technology Generation Assessing the East Asian Environmental Regime Matthew A. Shapiro Illinois Institute of Technology matthew.shapiro@iit.edu
  319. 319. Impetus • Shapiro and Nugent (2012) “Institutions and the sources of innovation” in IJPP • Total factor productivity is hindered by collaboration if institutions are absent or if not beyond TFP threshold • Shapiro (2013) “Regionalism’s challenge to the pollution haven hypothesis” in Pacific Review • Regional efforts to eliminate pollution are multifaceted • Support • East Asia Institute • Asiatic Research Institute, Korea University
  320. 320. International institutions To other regions To other regions Regional institutions Country 2 FDI Country 2 ecologists (+) Pollution haven hypothesis (+) (+) Epistemic community hypothesis (-) Country 1 pollution Country 2 pollution Country 3 pollution Country 1 institutions (-) Country 2 domestic R&D funding Country 3 domestic R&D funding Country 3 ecologists Country 3 FDI Contra-pollution haven hypothesis (-) Country 1 domestic R&D funding Country 1 ecologists Country 1 FDI Country 2 institutions Country 3 institutions
  321. 321. International institutions To other regions To other regions Regional institutions Country 2 FDI Country 2 ecologists (+) Pollution haven hypothesis (+) (+) Epistemic community hypothesis (-) Country 1 pollution Country 2 pollution Country 3 pollution Country 1 institutions (-) Country 2 domestic R&D funding Country 3 domestic R&D funding Country 3 ecologists Country 3 FDI Contra-pollution haven hypothesis (-) Country 1 domestic R&D funding Country 1 ecologists Country 1 FDI Country 2 institutions Country 3 institutions
  322. 322. International institutions To other regions To other regions Regional institutions Country 2 FDI Country 2 ecologists (+) Pollution haven hypothesis (+) (+) Epistemic community hypothesis (-) Country 1 pollution Country 2 pollution Country 3 pollution Country 1 institutions (-) Country 2 domestic R&D funding Country 3 domestic R&D funding Country 3 ecologists Country 3 FDI Contra-pollution haven hypothesis (-) Country 1 domestic R&D funding Country 1 ecologists Country 1 FDI Country 2 institutions Country 3 institutions
  323. 323. Research Questions • Are the Northeast Asian countries key collaborators in pursuit of green R&D? • Yes, particularly in recent years. • Are the Northeast Asian countries collaborating extensively with each other? • Not as much as they collaborate with countries beyond the region. • Implications?
  324. 324. Green R&D • Patents • IPC Green Inventory • • • • • • • Alternative energy production Transportation Energy conservation Waste management Agriculture/forestry Administrative aspects Nuclear power generation
  325. 325. Alternative energy production • Biofuels • Integrate gasification combined cycle • Fuel cells • Pyrolysis or gasification of biomass • Harnessing energy from manmade waste • Hydro energy • Ocean thermal energy conversion • Wind energy • Solar energy • Geothermal energy • Other production or use of heat not derived from combustion • Using waste heat • Devices for producing mechanical power from muscle energy Energy conservation • Storage of electrical energy • Power supply circuitry • Measurement of electricity consumption • Storage of thermal energy • Low energy lighting • Thermal building insulation, in general • Recovering mechanical energy
  326. 326. Data Collection • Source: USPTO • Collection method: Leydesorff’s tools • Unit of analysis: country of inventor
  327. 327. Data Description IL BE • Dates: 1990-2013 • 129,640 total inventors IN IT CN CH NZ TW all others AU KR DK • Assumption: Any collaboration is valued, so proportionate share of patent inventorship is ignored. CA GB • 242,331 total nodes based on country classification NL FR US DE JP
  328. 328. Are Northeast Asian countries key collaborators?
  329. 329. All years: 1990-2013
  330. 330. Longitudinal analysis…
  331. 331. 1990-1997
  332. 332. 1998-2004
  333. 333. 2005-2013
  334. 334. Is Northeast Asia a singular research hub?
  335. 335. All years: 1990-2013
  336. 336. Longitudinal analysis…
  337. 337. 1990-1997
  338. 338. 1998-2004
  339. 339. 2005-2013
  340. 340. Small world example
  341. 341. Northeast Asia only: 1990-2013
  342. 342. Implications • Empirical • R&D collaboration can be beneficial from both intra- as well as extra-regionally. Both are happening extensively for Northeast Asia. • Methodological • Challenges of connecting these results to other variables in model • Longitudinal concerns: Change in connectedness? • Qualitative, quantitative, mixed?
  343. 343. Assessing Social Media Coverage in Japan: Before and After March 11, 2011 Leslie M. Tkach-Kawasaki University of Tsukuba DISC 2013, December 11, 2013
  344. 344. Overview 1. 2. 3. 4. 5. 6. Introduction: Social Media in Japan 2010-2011 March 11, 2011: Triple Disaster Social Media: Before and After? Method Select Results (6 tables) Conclusion
  345. 345. Japan’s Internet Population 2011 Source: 2011 情報通信白書平成23年版
  346. 346. Social Media in Japan 2010-2011 Have used the following at least once….. Blogs  77.3% Video-sharing websites  62.8% SNS  53.6% Microblogs (Twitter)  30.9% Source: 2010 White Paper on Information and Communications in Japan
  347. 347. The Year in Social Media 2010-11 International diplomacy:Youtube and Chinese fishing vessel (September 2010)  Entertainment: Release of The Social Network (October 2010)  International conflicts: Role of Twitter and Facebook in Tunisia and Egypt (January 2011)  Disasters: New Zealand Earthquake (February 2011) 
  348. 348. And March 11, 2011….
  349. 349. Information Provision/Gathering During 2011 Earthquake Source: 2012 White Paper on Information and Communications in Japan
  350. 350. Research question…. Are there perceivable differences in the discourse (phrases) about social media in Japan’s newspaper media before and after March 11, 2011?

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