Big data divided (24 march2014)

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The Coming of Shadows in Big Data Research?
Widening and Narrowing Scholarly Divide

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Big data divided (24 march2014)

  1. 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. 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. 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. 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.
  5. 5. 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
  6. 6. 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)
  7. 7. http://pactlab-dev.spcomm.uiuc.edu/class/08SP/280/Diffusion-Certainty%20Lecture%20Notes.pdf
  8. 8. http://www.bbk.ac.uk/innovation/news-events/docs/s2/MEYER_new-triple-helix-environments.pdf
  9. 9. Mike Thelwall: WA 2.0 http://lexiurl.wlv.ac.uk/index.html
  10. 10. March Smith: NodeXL http://nodexl.codeplex.com/
  11. 11. http://thescrm.co.kr/ THE IMC’s SCRM
  12. 12. Han Woo PARK KrKWIC, WeboNaver, WeboDaum
  13. 13. 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.
  14. 14. 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 → ∽
  15. 15. • 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
  16. 16. Main Web Page of Twitaddons.com
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. Choi, S., & Park. H.W.@ (2014 Accepted). Networking interest and networked structure: A quantitative analysis of Twitter data. Social Science Computer Review*.
  21. 21. Choi, S., & Park. H.W.@ (2014 Accepted). Networking interest and networked structure: A quantitative analysis of Twitter data. Social Science Computer Review*.

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