The document discusses three potential divides that may emerge in big data research: 1) between developed and developing countries, 2) between academic and commercial sector researchers, and 3) between researchers with strong computational skills versus those with less computational skills. It provides examples of methods used in different country/region contexts and notes a critique of big data research around issues like changing definitions of knowledge, misleading claims of objectivity/accuracy, and new digital divides around data access.

1. The Coming of Shadows in Big Data Research?
Widening and Narrowing Scholarly Divide
Virtual Knowledge Studio (VKS)
Full 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
http://ct.kaist.ac.kr/iwsc2014 Int’l Workshop on Social Media and Culture 2014, KAIST

2. The Coming of Triple
Divide?
There are three main gaps I’d like to emphasize
in the present/future of research community:
1) Developing/Transitional VS
Developed/Advanced countries,
2) Researcher in academia VS Researcher in
commercial sector,
3) Researchers with computational skills VS
Less computational scholars.

3. Park, H.W.@, & Leydesdorff, L. (2013). Decomposing Social and Semantic Networks in
Emerging “Big Data” Research. Journal of Informetrics*. 7 (3), 756-765.

4. Method used Developed Country/
Region
Developing Country/
Region
Mixed Region
N % N % N %
Social-Informetics 114 74.51 30 83.33 9 52.94
Scientometrics 28 18.30 6 16.67 8 47.06
Webometrics 11 7.19 0 0 0 0
Total 153 100 36 100 17 100
No. of articles in each category of methods
by the developed/developing division
Skoric, M. M. (2013, Online First). The implications of big data for developing
and transitional economies: Extending the Triple Helix?. Scientometrics.

7. Meeting
Request : InnovAccer Hi Dr. Park,
 Hope you are doing well.
 I would like to introduce myself as Sachin Jaiswal, Co-founder and Head of Academic
Relations at InnovAccer, one of the fastest growing research acceleration firm. We have
been working with academicians from 35 of the top 100 universities across the globe
including researchers from Harvard, Wharton, Stanford, MIT, NUS, and INSEAD.
 We help researchers with Data Harvesting, Analytics, Visualization and Technology
Implementation. Our primary focus is to increase research productivity, reduce research
costs and enable researchers focus on the most important facets of their research. You
can read more about us here.
 After going through your research interests, I thought it would be a good idea to set up
some time for a short call and explore how we can help you accelerate your research. Let
me know a good time and we can schedule a call accordingly.
 I look forward to hearing from you.
 Regards,
Sachin Jaiswal
Co-Founder and VP
Academic Relations

10. Yet, there still are serious problems to overcome. A
trenchant critique concerning the big data field as it is
nowadays came in the form of six statements intending
to temper unbridled enthusiasm. [42] These six
provocative statements are:
 Big data change the definition of knowledge;
 Claims to accuracy and objectivity are misleading;
 More data are not always better data;
 Taken out of context, big data loses its meaning;
 Just because it is accessible, it does not make it ethical;
and
 (Limited) access to big data creates a new digital divide.
Rousseau (2012)

11. http://pactlab-dev.spcomm.uiuc.edu/class/08SP/280/Diffusion-Certainty%20Lecture%20Notes.pdf

13. http://www.bbk.ac.uk/innovation/news-events/docs/s2/MEYER_new-triple-helix-environments.pdf

14. Mike Thelwall: WA 2.0
http://lexiurl.wlv.ac.uk/index.html

15. March Smith: NodeXL
http://nodexl.codeplex.com/

16. http://thescrm.co.kr/
THE IMC’s SCRM

17. Han Woo PARK
KrKWIC, WeboNaver, WeboDaum

19. Today’s “big” is probably tomorrow’s “medium” and
next week’s “small” and thus the most effective def
ini-
tion of “big data” may be derived when the size of
data
itself becomes part of the research problem.
Big data sizes may vary per discipline.

20. Big Data and Social Webometrics Network Analysis
Increasing data size in terms
of the no. of nodes
Micro ≦100 nodes →10K
Meso ≦1000 nodes →1000K
Macro ≦10000 nodes
→100,000K
Super-
Macro
≥10000 nodes → ∽

21. • Micro: Individual’s action in posting,
replying, subscribing, following, replying,
retweeting, mentioning, liking, hyperlinking,
joining, friending, etc.
• Meso: Relationship among them
• Macro: Impact of the inter-relationship to
community’s overall socio-cultural network
structure

22. Main Web Page of Twitaddons.com

23. 23
Political Conversation Content Spreading
Chopae
MBC
Common
Sense
NodeXL, Data period: Mar. – Sept. 2010
party organizers
party members
followers of the
party organizer
Preliminary study of Twitaddons.com
Two-mode network visualization by party
Choi, S., & Park, H. W.@ (2014). An exploratory approach to a Twitter-based community centered on a political goal in
South Korea: Who organized it, what they shared, and how they acted. New Media & Society*. 16 (1). 129-148

24. party organizers
party members
followers of the
party organizer
24
Commercial Conversation Content Spreading
Blackberry
Android
Official HTC
Korean HTC
Preliminary study of Twitaddons.com
Two-mode network visualization by party

25. 25
Social Conversation Content Spreading
Tourism
Innovation
Welfare
Volunteer
Food Car
party organizers
party members
followers of the
party organizer
Preliminary study of Twitaddons.com
Two-mode network visualization by party

26. Choi, S., & Park. H.W.@ (2014 Accepted). Networking interest and networked structure:
A quantitative analysis of Twitter data. Social Science Computer Review*.

27. Choi, S., & Park. H.W.@ (2014 Accepted). Networking interest and networked structure:
A quantitative analysis of Twitter data. Social Science Computer Review*.