1.
빅데이터 시대의
미디어&커뮤니케이션 교육과 연구
박한우 (영남대 교수)

2.
언론학회 기획테마세션 관련
교수님께서 참여하시는 세션은 이번 봄철학술대회 대주제를 반영한 기획 세션이고, 일정 등은 다음과 같습니다.
주제: 미디어 생태계 변형과 미래 언론학
일자: 2016년 5월 21일(토)
장소: 이화여자대학교
시간: 4부(15:50~17:30, 100분)
발표: 이동후(인천대), 박한우(영남대), 강홍렬(정보통신연구원)
4부(15:50~17:30)에 100분간 진행되며, 장소는 이화여대 ECC B225호입니다(날짜: 5월 21일 토).
100분동안 3편의 논문이 발표되며, 각 논문 당 한 분이 토론에 참여하십니다.
여유있는 토론이 이루어지도록 시간을 넉넉히 잡으려 했는데, 막상 그렇게 여유있지는 않아 보이네요...;;;;

3.
발표의 구성과 내용
• 언론정보학의 미래를 어떻게 볼 것인가?
- 외삽주의(Extrapolationist): 과거부터 현재까지 변화추세
가 미래로 연결됨; 보수학파
- 급진주의: 새로운 사회는 총체적 위기나 혼란으로부터 만
들어짐; 불연속&단절학파
- 전이주의: 오랜 기간을 거쳐 확산되는 점진적 변화가 이루
어짐; 메가트렌드학파
참고: 최연구, 미래학의 연구기법과 미래학자

4.
발표의 구성과 내용
• Old future: Helena Norberg-Hodge의 《오래된 미래 Ancient
Futures》에서 시작됨
• Present future: David Carr의 2014년 보스톤대학의 강의제목
에서 시작됨 Press Play: Making and distributing content in
the present future we are living through
• Strategic future: Peter Drucker의 “The best way to predict
the future is to create it.”
- 한국정보화진흥원 Near & Future 현상에서 미래를 보다: 빅
데이터분석 기반 환경스캐닝을 통해 미래예측과 전략수립의
단서를 찾기

5.
WHAT ARE WE TEACHING AND
INVESTIGATING?
Where are we?

6.
Theories in Communication Science using Google Scholar: A Structural Analysis Using Webometrics and
Social Network Approaches
Nodes are theories, node size represents occurrence (size is bigger if a theory co-occurred more frequently with other theories)
link represents co-occurrence relationship. Overlapping nodes label are not shown. Nodes color represents clustering based on
occurrence. The nodes the co-occure together more frequently are assigned same colors.
Health-Related
Phenomenon
Mass-
Communication-
related
Theory
Communication Uses &
Process in the context of
CMC
Interpersonal-
Organizational com

7.
OPEN AND BIG DATA
What Surrounds US Shapes US

8.
http://www.kdnuggets.com/images/cartoon-dog-big-data.jpg

9.
강장묵 (2015). 프라이버스 보호기술의 진화와 인간중심적 설계 방향. 인문사회융합 동향Focus & Review, 10호 1권, 3월호. 11-19

10.
21
• Han Woo Park
- “hidden” and “relational” data about web-based
content (e.g., text, images, audio-visual objects, and
hyperlinks) and actors including individuals, groups,
and nation-states
• 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)

11.
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 → ∽
출처: 박한우(2014)

12.
“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’

13.
How different across disciplines?

14.
BARRIERS TO OVERCOME
Problems & Issues

15.
박한우, 소셜 여론조사의 실제와 과제- ‘저비용 고효율’ SNS로 여론 읽기. 월간
<신문과 방송>, 2012년 7월. 84-88쪽.

16.
President Harry S. Truman holds up a copy of the Tribune after his
presidential election, arguably one of the most famous headline mistakes.

17.
Kobayashi, T., & Boase, J. (2012). No Such Effect? The Implications of Measurement Error in Self-Report Measures of
Mobile Communication Use. Communication Methods and Measures, 6, 1–18. DOI: 10.1080/19312458.2012.679243

18.
Why Data Science?
Savage and Burrows (2007, p. 886) lament,
“Fifty years ago, academic social scientists
might be seen as occupying the apex of
the – generally limited – social science
research ‘apparatus’. Now they occupy an
increasingly marginal position in the huge
research infrastructure”.
Bonacich, P. (2004).
The Invasion of the Physicists. Social Networks 26(3): 285-288

19.
"데이터 분석과 저널리즘“ 함형건 (2015.1)
VS "데이터 저널리즘“ 임종섭 (2015.3)

20.
All models are wrong but some are useful
Emergence of data author on dataverse

21.
The Signal and the Noise:
Why Most Predictions Fail but Some Don't. Nate Silver
I do not go as far as a Popper in asserting that such theories are
therefore unscientific or that they lack any value. However, the fact that
the few theories we can test have produced quite poor results suggests
that many of the ideas we haven’t tested are very wrong as well. We are
undoubtedly living with many delusions that we do not even realize.
page 15

22.
http://www.nature.com/nature/journal/v455/n7209/
4 September 2008 Volume 455 Number 7209 pp1-136
"what Big Data sets mean for
contemporary science”

23.
Science published a special issue
(February 11, 2011) looking broadly at
increasingly data-driven research efforts
as a scientific domain (Science staff,
2011).
Data Science is composed of interrelated clusters of
research tasks. For example, the technologies on data
collection, curation, and access, and the unique skill
sets have increasingly been central to Data Science
(Science staff, 2011).

24.
Economics in the age of big data
http://www.sciencemag.org/content/346/6210/1243089.full

25.
http://bigdatasoc.blogspot.co.uk/

26.
http://bds.sagepub.com/content/1/1/2053951714540280.full

27.
http://www.nae.edu/Publications/Bridge/128772.aspx
Winter Bridge: A Global View of Big Data

28.
http://www.nae.edu/Publications/Bridge/128772.aspx
Winter Bridge: A Global View of Big Data

29.
출처: 이영주 (2015). 빅데이터 시대, 미래전략의 새로운 접근법. NIA. 14호.

30.
WHAT TO TEACH AND HOW?
Futures & Agendas

31.
Famous Quote From
Sir Isaac Newton
“If I have been able to see farther than
others, it was because I stood on the shoulders of giants.”
45
거인의 어깨에 올라서서
더 넓은 세상을 바라보라
- 아이작 뉴턴 -

32.
Six Sources of Big Data
• Web Mining
• Search Information
• Social Media
• Crowd Sourcing
• Transactional Data
• Mobile
https://datafloq.com/read/how-big-data-drives-digital-marketing-success/1469

33.
오늘날 연구자와 기업들은 건초더미에서 바늘을 찾는 대신, 우주 안에서 건초더미
를 찾고 그 건초더미가 새로운 분야의 시작인지 아니면 작년 수확물의 부산물인지
예측해야함
가치창조형 정보분석은 바늘찾기 다

34.
Mike Thelwall: WA 2.0
http://lexiurl.wlv.ac.uk/index.html

35.
March Smith: NodeXL
http://nodexl.codeplex.com/

36.
Han Woo PARK
KrKWIC, WeboNaver, WeboDaum

37.
https://wiki.digitalmethods.net/Dmi/ToolDatabase

38.
http://discovertext.com/

39.
https://netlytic.org/

40.
http://chorusanalytics.co.uk/

41.
https://support.google.com/fusiontables/answer/2571232

42.
http://www.tableau.com/

43.
https://pipes.yahoo.com/pipes/

44.
http://www.kapowtech.com/

45.
http://www.qgis.org/ko/site/

46.
ArcGIS 를 이용한 오픈데이터 툴. 세계은행 데이터 등 cool

47.
http://www.ibm.com/smarterplanet/us/en/ibmwatson/discovery-advisor.html

48.
https://www.youtube.com/watch?v=DS310JMdu2s

49.
https://www.youtube.com/watch?v=DS310JMdu2s

50.
뷰어태리어트: 공모전 수상

51.
뷰어태리어트: 공모전 수상
https://www.youtube.com/watch?v=2wgewvwjAJM

52.
붉은 여왕 효과와 지방언론
http://zezecool.tistory.com/17