O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.
Webometrics and
Studies of Cultural Diffusion
-Psy Gangnam Style on YouTube
(朴漢雨)
Korean Wave (Hallyu, 韩流)
Huallywood (华莱坞)
Cultural globalization
Dress Pants From the
Star
Yang Pu Style
A new form of cultural globalization?
Cultural diffusion supported by Web 2.0 applications show two
distinguishable characteristics:
How cultural diffusion in d...
Webometrics is the study of quantitative aspects of
internet communication (Almind & Ingwersen, 1997)
With regard to socia...
Introducing a series of webometric studies that look at
new elements in cultural diffusion:
Study one: Web ecosystem that ...
Case study: Gangnam Style on YouTube
Gangnam Style was the most
watched YouTube video by 2012
Annals of Technology
Streaming Dreams: YouTube turns pro.
On TV, airtime is a scarce resource; on YouTube, it’s infinite
http://www.youtube.com/user/TheKARAOKEChannel
Study one: the structure and content of diffusion ecosystem
Three elements are salient
• Actors (users)
• Network (YouTube...
Study one: an integrated webometric model
Xu, W. W., Park, J. Y., & Park, H. W. (2015). The networked cultural
diffusion o...
Study one: research questions
Question 1: What are the demographic and behavioral
characteristics of the actors in YouTube...
Data collection: API-based analysis program Webometric Analyst 2.0 used to
download 1,000 comments posted to Psy’s Gangnam...
Big Data and Social Webometrics Network Analysis
Increasing data size in terms of the
no. of nodes
Micro ≦100 nodes →10K
M...
Study one: Data description
Analyses:
• Profile: focusing on user-disclosed age, location (used as a proxy for
cultural id...
Study one: Findings
• 69% commenters are males
• The average age: 23.5
• U.S. (47%), UK (7%), Canada (7%),
Korea (4%), Net...
Study one: Findings
Study one: Findings
Study one: Findings
• The more culturally distant from Korea in terms of power
distance, the less likely the positive sent...
RELATED STUDIES ON THIS TOPIC
International Network
South Korea
Thailand
Undisclosed
Twitter network analysis of
Busan International Film Festival (BIFF...
mickyworld
(K-POP fan account)
busanfilmfest
(BIFF official Twitter account)
Result : Network Analysis
ThailandSouth Korea...
Spearman Correlations between Variables
Cultural
Proximity
Twitter
Penetrati
on Rate
K-POP
Diffusion
BIFF
Twitter
user
n.s...
Cross-Cultural Analysis of
Beehive Status Messages within IBM
Previous familiarity with the
characteristics of other SNSs ...
한일 트위터 비교
# Code Example(s)
1 Information Sharing (IS)
: 정보나누기
“15 Impressive and Beautiful Uses of WordPress <URL REMOVED...
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
IS SP OC RT ME QF PM AM AO
Proportionofall
Messages
Korea
Japan
The result of Content analysis of Korean and
Russian Tweets
0
10
20
30
40
50
IS SP OC RT ME QF PM AM AO
25.1
0.6
11.0
38.4...
Message category frequency
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
IS SP OC RT ME QF PM AM AO
Proportionofall
Messages
...
• Actors: The actors in the diffusion of the GS video were young YouTube
users living in North America and Europe.
• Relat...
• A meme is an idea, behavior, style, or structure that spreads from one
person to another within a given culture (Dawkins...
Some examples of meme inspired by Gangnam style
Study two: a memetic ecosystem
Defining connections between memetic objects:
• Two memetic videos are connected when two videos draw
attention and action...
Related studies on this topic
First type of Webometrics
• Hyperlink Network Analysis
- Inter-linkage: who linked to whom matrix
- Co-inlink: a link to t...
Inter-link network analysis diagram among Korean e-science sites within
public domain
Mapping the e-science landscape
In S...
Co-inlink network analysis
Mapping the e-science landscape
In South Korea using the Webometrics method
WCU
WEBOMETRICS
INS...
WCU
WEBOMETRICS
INSTITUTE
INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS
Case 2. Cyworld Mini-hompies of Kore...
• RQ1: What video genres are inspired by the original GS video and how
salient is each genre and the actor (source, author...
• "Gangnam Style" was used to extract videos with titles,
keywords, descriptions, categories, or usernames
matching the ke...
Study two: meme types
Study two: meme network
Note: August data
Study two: meme network
Note: September data
* Sample: Review Video
Source: http://www.youtube.com/watch?v=uerYj6KudeY
* Sample: Reaction Video
Source: https://www.youtube.com/watch?v=b-KX6GB5oCE
Top by degree and betweenness centrality in August
Study two: top memetic videos
Top by degree and betweenness centrality ...
Study two: in a nutshell
• The viral GS video sparked a sizable amount of user creativity
manifested in different forms of...
Study three: the network evolution
A longitudinal approach based on analytical framework laid
out in study one
RQ1: What a...
Study three: findings
• Visually, the hub-and-spoke structure was more prominent in the Time 1 network and became less so
...
Related studies on this topic
54
Changes of co-link networks during presidential
campaign period
• Co-(in)link analysis of the 20 websites of the
candid...
55
2 Dec 2007
11 Dec 2007
17 Dec 2007
56
Network measures 2 Dec 07 11 Dec 2007 17 Dec 2007
Clustering coefficient 2.581 2.368 1.777
Average distance
(Cohesion v...
Result - 1
Time (from 24th May to 2nd June)
Mayors
Educational
superintendents
Web Ecology - 2011 ICA 5/29/2011
Date
Link(2010_M)
N=44
Link(2010_E)
N=69
Link(2007_P)
N=20
Date
24-May-10 3.77 0.03
25-May-10 3.82 0.04
26-May-10 3.86 0.0...
24May2010
Education Superintendents VS Mayors
25May2010
Education Superintendents VS Mayors
26May2010
Education Superintendents VS Mayors
27May2010
Education Superintendents VS Mayors
28May2010
Education Superintendents VS Mayors
30May2010
Education Superintendents VS Mayors
31May2010
Education Superintendents VS Mayors
1June2010
Education Superintendents VS Mayors
2June2010
Education Superintendents VS Mayors
68
Myunggoon Choi , Yoonmo Sang , Han Woo Park , (2014) "Exploring political discussions by Korean twitter users: A look a...
Web 1.0
2000
2001
‣ 59 isolated in 2000
‣ more centralised in 2001
‣ network of 2001 ➭ a ‘star’ network
- might affected b...
Web 2.0
2005 2006
‣hubs disappearing
‣easy use of blogs
‣Clear boundaries between different parties
‣strong presence of GN...
Politician Twitter Network (Following and 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)
Viewertariat Networks:
A Study of the 2012 South Korean Presidential Debate
Moon’s network
Park’s network
Reply-To Networks of Park’s & Moon’s Facebook page
visitors during TV debates
Study three: findings
Study three: in a nutshell
• The interest in commenting on the GS video was intensive shortly after
the release of the vid...
The Big Picture
• In studying cultural diffusion in the digital age, we need to
focus on:
• Not only virality but also mem...
Related studies on this topic
트위터의 Kpop 해쉬태그 분석
가수 미디어/채널 한국/한류 기타 국가 일반 기타 합계
일본
3,866
(32.0%)
396
(3.3%)
3,570
(29.6%)
443
(3.7%)
2,320
(19.2%)
1,486
...
North_America (미국, 캐나다)
N=896
South_America (멕시코 브라질 콜롬비아 페루)
N=774
Europe (독일 영국 스페인 프랑스)
N=812
유럽의 사례
 유럽의 트위터 K팝 네트워크
- K팝 콘서트가 열린 프랑스가 K팝 확산의 주요
허브로 기능
- 다음으로 영국, 독일, 스페인, 이탈리아
- 동구 유럽의 경우 트위터 상에서 K팝 관련 담
화가 매우 부족
히스패닉의 사례
 시계열적 자료 수집을 통해 히스패닉
문화권의 트위터 K팝 네트워크의 성
장과 변화를 확인
Japan 일본
N=1744
Rest of Asia 아시아 기타지역
N=874
Indonesia 인도네시아
N=1588
Future collaboration
• Applying webometrics to study the diffusion of Chinese pop
culture on Web 2.0
Reach me at hanpark@y...
http://bigdatasoc.blogspot.co.uk/
Webometrics and Studies of Cultural Diffusion-Psy Gangnam Style on YouTube
Webometrics and Studies of Cultural Diffusion-Psy Gangnam Style on YouTube
Webometrics and Studies of Cultural Diffusion-Psy Gangnam Style on YouTube
Webometrics and Studies of Cultural Diffusion-Psy Gangnam Style on YouTube
Webometrics and Studies of Cultural Diffusion-Psy Gangnam Style on YouTube
Webometrics and Studies of Cultural Diffusion-Psy Gangnam Style on YouTube
Webometrics and Studies of Cultural Diffusion-Psy Gangnam Style on YouTube
Próximos SlideShares
Carregando em…5
×

Webometrics and Studies of Cultural Diffusion -Psy Gangnam Style on YouTube

4.147 visualizações

Publicada em

Webometrics and Studies of Cultural Diffusion -Psy Gangnam Style on YouTube

Publicada em: Diversão e humor
  • Entre para ver os comentários

Webometrics and Studies of Cultural Diffusion -Psy Gangnam Style on YouTube

  1. 1. Webometrics and Studies of Cultural Diffusion -Psy Gangnam Style on YouTube (朴漢雨)
  2. 2. Korean Wave (Hallyu, 韩流) Huallywood (华莱坞) Cultural globalization
  3. 3. Dress Pants From the Star Yang Pu Style A new form of cultural globalization?
  4. 4. Cultural diffusion supported by Web 2.0 applications show two distinguishable characteristics: How cultural diffusion in digital age is different 1. Networked diffusion: word-of-mouth, viral diffusion across online social networks 2. Internet meme: produser culture, creative reassembling and recreation of old cultural symbols. Examples: parody video, kuso, sproof, 恶搞,remix
  5. 5. Webometrics is the study of quantitative aspects of internet communication (Almind & Ingwersen, 1997) With regard to social media communication, webometrics has evolved to incorporate a set of methods: • Social network analysis • Automated content analysis (theme-detection, sentiment, semantic analysis) • Traditional content analysis How webometrics can help
  6. 6. Introducing a series of webometric studies that look at new elements in cultural diffusion: Study one: Web ecosystem that supports dissemination of cultural offerings Study two: Development of internet meme Study three: Longitudinal approach to Web 2.0-based cultural diffusion How scholars should respond to this new trend
  7. 7. Case study: Gangnam Style on YouTube Gangnam Style was the most watched YouTube video by 2012
  8. 8. Annals of Technology Streaming Dreams: YouTube turns pro. On TV, airtime is a scarce resource; on YouTube, it’s infinite
  9. 9. http://www.youtube.com/user/TheKARAOKEChannel
  10. 10. Study one: the structure and content of diffusion ecosystem Three elements are salient • Actors (users) • Network (YouTube reply and subscription relationships) • Message (comments) Accordingly, • Profile analysis • Network analysis • Content analysis
  11. 11. Study one: an integrated webometric model Xu, W. W., Park, J. Y., & Park, H. W. (2015). The networked cultural diffusion of Korean Wave. Online Information Review, 39(1).
  12. 12. Study one: research questions Question 1: What are the demographic and behavioral characteristics of the actors in YouTube-based cultural diffusions? Question 2: What are the characteristics of peer interactions and shared interest among the actors in YouTube-based cultural diffusions? Question 3: What opinions are expressed by the actors in their evaluation of a cultural offering on YouTube?
  13. 13. Data collection: API-based analysis program Webometric Analyst 2.0 used to download 1,000 comments posted to Psy’s Gangnam style video in August 2012 (a month after the initial release of the video). The sample has 983 valid comments contributed by 534 users. At the time of data-collection, there were 93,884 total comments. Study one: Data description
  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 → ∽ 출처: 박한우(2014)
  15. 15. Study one: Data description Analyses: • Profile: focusing on user-disclosed age, location (used as a proxy for cultural identity), and gender • Network: two types of network ties, one based on YouTube subscription (A and B are connected when both subscribe to a same YouTube channel), another based on reply-to-comment (A and B are connected when A replies to B’s comment or vice versa) • Content: sentiment (a scale ranging from -5 to 5, with positive numbers indicating favorable attitudes); semantics (occurrence of keywords in comments)
  16. 16. Study one: Findings • 69% commenters are males • The average age: 23.5 • U.S. (47%), UK (7%), Canada (7%), Korea (4%), Netherlands (3%)…
  17. 17. Study one: Findings
  18. 18. Study one: Findings
  19. 19. Study one: Findings • The more culturally distant from Korea in terms of power distance, the less likely the positive sentiment toward the GS. • The more dissimilar the country to Korea in terms of both individualism and masculinity, the more likely the negative comment.
  20. 20. RELATED STUDIES ON THIS TOPIC
  21. 21. International Network South Korea Thailand Undisclosed Twitter network analysis of Busan International Film Festival (BIFF) 2014 -Is the BIFF really getting global?-
  22. 22. mickyworld (K-POP fan account) busanfilmfest (BIFF official Twitter account) Result : Network Analysis ThailandSouth Korea The highest In-Degree score Node of each country
  23. 23. Spearman Correlations between Variables Cultural Proximity Twitter Penetrati on Rate K-POP Diffusion BIFF Twitter user n.s. n.s. 0.454* *Significant at p < 0.05 n.s. = Not Significant Definition Measurement Cultural Proximit y How different South Korea and the other countries in terms of cultural traits. Hofstede`s cultural dimensions. Twitter Penetrati on Rate The rate of circulation of a Twitter in a specific population. Number of Twitter users per country. K-POP Diffusion The degree of the spreading of Korean popular music in a specific population. Exposure to K-POP songs and/or Consumption of K- POP products per country (e.g., Korean music album sales and downloads, etc.)
  24. 24. Cross-Cultural Analysis of Beehive Status Messages within IBM Previous familiarity with the characteristics of other SNSs may be influencing how users behave on Beehive Users in high power distance may use the status messages more for indicating general career interests and skills, rather than time-based updates of what one is doing or how one is feeling
  25. 25. 한일 트위터 비교 # Code Example(s) 1 Information Sharing (IS) : 정보나누기 “15 Impressive and Beautiful Uses of WordPress <URL REMOVED>” 신간소개 위대한 역사도시 70 존 줄리어서 노리치 엮음 남경태 옮김 도시의 형성 과정과 특징 독특한 문화유산 번영과 몰락의 과정소개 한국경제 보도기사 http://j.mp/cvNe24 -웹디자이너분들 필독!!! RT @sentv_kr: 서울경제TV에서 함께 할 정규직 웹디자이너를 구합니다. 무한 RT 부탁드립니다.http://gil.cc/8Vgd 2 Self Promotion (SP) : 자기홍보 “Check out my blog I updated 2day 2 learn abt tuna! <URL REMOVED>” “방금 내 슬라이드쉐어에 트위터발표문업데이트 보러오세요. http://slidesha.re/dfOXNX” 3 Opinions/Complaints (OC) :의견/불평 “Go Aussie $ go!”,“Illmatic= greatest rap album ever” “그림도 그림이지만 좀더 다양한 연출을 하고 싶다. 너무 진부하게 딱딱할 정도로 맞춰져서 꽉 들어차있기만 한 연출은 아무리 봐도 재미없다” ”군대 안갔다온 이명박의 정권이 하는짓은 군사정권때 하던짓을 하려한다. 4대강 사업에 군대를 동원하다니..”“통일세 같은소리 하고 자빠졌네” 4 Statements and Random Thoughts (RT) :무작위적인 생각들 “The sky is blue in the winter here” ”I miss New York but I love LA... ”휴~ 오늘도 아직 별일 없이 살아있다” “과메기 먹고싶다” 5 Me now (ME) :현재자신의 하고있는 일이나 감정장 소말하기 “tired and upset” “just enjoyed speeding around my lawn on my John Deere. Hehe :)” “불꽃놀이보러왔는데 보기전에 얼어죽을거같다 으윽” “졸리고 피곤해서 집에가자마자 폭풍수면.....”_I'm at 정부과천청사. http://4sq.com/deggpH” 6 Question to followers (QF): 자기팔 로워에게 질문하기 “what should my video be about?” “음.. 아래 친구분들이 남기신 글에는 왜 댓글 쓰기가 안되는 걸까요? “ “어디 담배 쉽께 끊는 방법 없나요?ㅜ.ㅠ” “리플이랑 리트윗의 차이가 어떻게 되나요?” 7 Presence Maintenance (PM) :Twitter 에서의 현재상태 말하기 “i'm backkkk!” “gudmorning twits” “#소중하당_[저왔어요_。] 넬름. 왔다 ㅌㅌㅌ” “#사랑한당_ 그럼요 저도 출석체크를 해야지요” “소중하당_[저왔어요_。] 왔어요 왔다니까요.” 8 Anecdote (me) (AM) :자기일화말하기 “oh yes, I won an electric steamboat machine and a steam iron at the block party lucky draw this morning!” 조찬 회의를 마치고 회사로 복귀하려고 엘리베이터에서 내리는 순간 전진삼 선생님께서 맞이해주셨다. 언제나 밝게 대해주시는 선생님... 9 Anecdote (others) (AO) :타인의일화말하기 “Most surprised <user> dragging himself up pre 7am to ride his bike! He usually doesn't get up that early for anything!” “오늘아침 횡단보도에서 새벽에 무언가를 외우며 운동화를 신고 빠른 걸음으로 걸어가네요. 스터디하러가는가봐요”
  26. 26. 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 IS SP OC RT ME QF PM AM AO Proportionofall Messages Korea Japan
  27. 27. The result of Content analysis of Korean and Russian Tweets 0 10 20 30 40 50 IS SP OC RT ME QF PM AM AO 25.1 0.6 11.0 38.4 20.1 2.8 0.3 1.3 0.2 38.2 0.5 4.9 34 14.8 1.5 1 4.8 0.4 Korea Russia
  28. 28. Message category frequency 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 IS SP OC RT ME QF PM AM AO Proportionofall Messages Korea Japan US
  29. 29. • Actors: The actors in the diffusion of the GS video were young YouTube users living in North America and Europe. • Relationship: Interactions flowed between a small set of users, and most users represented a silent majority who only subscribed to the video channel but did not interact with other users. • Content: Commenters were interested in the cultural origin of the video and related media content to the broader national and cultural image of a foreign country/ Users whose cultural background is similar to Korean culture are more likely to favor GS video. Study one: Results in Summary
  30. 30. • A meme is an idea, behavior, style, or structure that spreads from one person to another within a given culture (Dawkins, 1976). • Meme on YouTube: remix culture on YouTube (Burgess & Green, 2013). GS has sparked memetic creativity • GS-inspired meme: horse-dance style, music, lyrics, and clothing style, among others. Remix, parody, self-directed performance and review derived from GS. • Memetic ecosystem: an environment where cultural consumers review, resemble and recreate old cultural components to forge new ideas and products. Study two: a memetic ecosystem
  31. 31. Some examples of meme inspired by Gangnam style Study two: a memetic ecosystem
  32. 32. Defining connections between memetic objects: • Two memetic videos are connected when two videos draw attention and actions from the same user (videos A and B are tied when both are commented on by the same user) Study two: how to study internet meme network
  33. 33. Related studies on this topic
  34. 34. 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)
  35. 35. Inter-link network analysis diagram among Korean e-science sites within public domain Mapping the e-science landscape In South Korea using the Webometrics method WCU WEBOMETRICS INSTITUTE
  36. 36. Co-inlink network analysis Mapping the e-science landscape In South Korea using the Webometrics method WCU WEBOMETRICS INSTITUTE
  37. 37. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Case 2. Cyworld Mini-hompies of Korean Legislators Figure 4: Cyworld Mini-hompies of Korean legislators 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. How do social scientists use link data from search engines to understand Internet-based political and electoral communication?
  38. 38. • RQ1: What video genres are inspired by the original GS video and how salient is each genre and the actor (source, authority, or hub) represented by the genre? • RQ2: What video genres have better drawn viewers’ collective attention and engagement? • RQ3: Based on network positions, what videos and actors they represent are more likely to influence other videos? • RQ4: How does the salience of each video genre change over time? • RQ5: Based on the ability to draw viewers’ collective attention and engagement, how does the influence of each video genre change over time? • RQ6: How does the centrality of each video change over time? Study two: research questions
  39. 39. • "Gangnam Style" was used to extract videos with titles, keywords, descriptions, categories, or usernames matching the keyword. • 628 clips were included in the sample for August; 841, for September; and 665, for October. Study two: Data description
  40. 40. Study two: meme types
  41. 41. Study two: meme network Note: August data
  42. 42. Study two: meme network Note: September data
  43. 43. * Sample: Review Video Source: http://www.youtube.com/watch?v=uerYj6KudeY
  44. 44. * Sample: Reaction Video Source: https://www.youtube.com/watch?v=b-KX6GB5oCE
  45. 45. Top by degree and betweenness centrality in August Study two: top memetic videos Top by degree and betweenness centrality in September
  46. 46. Study two: in a nutshell • The viral GS video sparked a sizable amount of user creativity manifested in different forms of user-generated content created. • Different modes of cultural imitation and recreation may draw disproportional levels of audience attention and engagement. • Traditional mass media continued to be prominent in the memetic cultural ecosystem.
  47. 47. Study three: the network evolution A longitudinal approach based on analytical framework laid out in study one RQ1: What are the longitudinal trends in the type of actors in the YouTube-based cultural diffusion of Kpop? RQ2a: What are the longitudinal trends in YouTube networks based on users’ reply-to activities? RQ2b: What are the longitudinal trends in YouTube networks based on users’ co-subscriptions? RQ3a: What are the longitudinal trends in the semantics of comments in the YouTube-based cultural diffusion of Kpop? RQ3b: What are the longitudinal trends in sentiments in comments in the YouTube-based cultural diffusion of Kpop?
  48. 48. Study three: findings • Visually, the hub-and-spoke structure was more prominent in the Time 1 network and became less so in the following months. • Across the three time points, the network based on reply-to activities fragmented gradually, and commenters became more independent. • Across the three time points, the network based on reply-to activities fragmented gradually, and commenters became more independent • Time 1 (August) Time 2 (September), Time 3 (October)
  49. 49. Related studies on this topic
  50. 50. 54 Changes of co-link networks during presidential campaign period • Co-(in)link analysis of the 20 websites of the candidates/parties using the Yahoo – Also web size, incoming links, visitor traffic • Qualitative complements • Particularly usefulness: Public opinion surveys could not be published within six days before the 2007 election
  51. 51. 55 2 Dec 2007 11 Dec 2007 17 Dec 2007
  52. 52. 56 Network measures 2 Dec 07 11 Dec 2007 17 Dec 2007 Clustering coefficient 2.581 2.368 1.777 Average distance (Cohesion value) 1.564 (0.215) 1.821 (0.273) 1.681 (0.346) Degree centralities of sites ijworld.or.kr leehc.org ckp.kr 0.158 0.000 0.000 0.263 0.053 0.053 0.684 0.263 0.053 Network Measures with Three Different Points
  53. 53. Result - 1 Time (from 24th May to 2nd June) Mayors Educational superintendents Web Ecology - 2011 ICA 5/29/2011
  54. 54. Date Link(2010_M) N=44 Link(2010_E) N=69 Link(2007_P) N=20 Date 24-May-10 3.77 0.03 25-May-10 3.82 0.04 26-May-10 3.86 0.04 27-May-10 3.77 0.11 869.66 02-Dec-07 28-May-10 3.62 0.15 785.52 05-Dec-07 30-May-10 3.87 0.63 877.92 08-Dec-07 31-May-10 3.92 0.92 940.58 11-Dec-07 01-Jun-10 4.03 1.24 819.72 14-Dec-07 02-Jun-10 4.10 1.36 1129.62 17-Dec-07
  55. 55. 24May2010 Education Superintendents VS Mayors
  56. 56. 25May2010 Education Superintendents VS Mayors
  57. 57. 26May2010 Education Superintendents VS Mayors
  58. 58. 27May2010 Education Superintendents VS Mayors
  59. 59. 28May2010 Education Superintendents VS Mayors
  60. 60. 30May2010 Education Superintendents VS Mayors
  61. 61. 31May2010 Education Superintendents VS Mayors
  62. 62. 1June2010 Education Superintendents VS Mayors
  63. 63. 2June2010 Education Superintendents VS Mayors
  64. 64. 68 Myunggoon Choi , Yoonmo Sang , Han Woo Park , (2014) "Exploring political discussions by Korean twitter users: A look at opinion leadership and homophily phenomenon", Aslib Journal of Information Management, Vol. 66 Iss: 6, pp.582 - 602
  65. 65. Web 1.0 2000 2001 ‣ 59 isolated in 2000 ‣ more centralised in 2001 ‣ network of 2001 ➭ a ‘star’ network - might affected by political events ➭ presidential election in 2001
  66. 66. Web 2.0 2005 2006 ‣hubs disappearing ‣easy use of blogs ‣Clear boundaries between different parties ‣strong presence of GNP Assembly members ➭ party policy on using blogs
  67. 67. Politician Twitter Network (Following and Mention Network)
  68. 68. 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
  69. 69. Affiliation network diagram using pages linked to Lee’s and Park’s sites N = 901 (Lee: 215, Park: 692, Shared: 6)
  70. 70. Viewertariat Networks: A Study of the 2012 South Korean Presidential Debate Moon’s network Park’s network
  71. 71. Reply-To Networks of Park’s & Moon’s Facebook page visitors during TV debates
  72. 72. Study three: findings
  73. 73. Study three: in a nutshell • The interest in commenting on the GS video was intensive shortly after the release of the video. • influential commenters remained relatively consistent over time, implying that once users established credibility or authority, their influence tended to persist. • Commenters were generally young and male. • Based on semantics and sentiments, the GS video, its artist, and the underlying cultural phenomenon were evaluated against other figures and shows in popular culture.
  74. 74. The Big Picture • In studying cultural diffusion in the digital age, we need to focus on: • Not only virality but also meme • Take a web ecology perspective by using webometric approach to examine different elements in the diffusion
  75. 75. Related studies on this topic
  76. 76. 트위터의 Kpop 해쉬태그 분석 가수 미디어/채널 한국/한류 기타 국가 일반 기타 합계 일본 3,866 (32.0%) 396 (3.3%) 3,570 (29.6%) 443 (3.7%) 2,320 (19.2%) 1,486 (12.3%) 12,081 (100%) 인도네시아 92 (5.5%) 108 (6.4%) 363 (21.6%) 29 (1.7%) 1,052 (62.6%) 36 (2.1%) 1,680 (100%) 기타 아시아 405 (8.2%) 4,049 (81.6%) 84 (1.7%) 135 (2.7%) 291 (5.9%) 0 (0.0%) 4,964 (100%) 북미 774 (13.1%) 4,569 (77.3%) 174 (2.9%) 69 (1.2%) 262 (4.4%) 62 (1.0%) 5,910 (100%) 남미 365 (45.6%) 19 (2.4%) 53 (6.6%) 222 (27.7%) 97 (12.1%) 45 (5.6%) 801 (100%) 유럽 300 (35.1%) 240 (28.1%) 50 (5.9%) 20 (2.3%) 110 (12.9%) 134 (15.7%) 854 (100%)  지역별로 트위터 상의 Kpop 관련 해쉬태그 분석 - 대체적으로 가수의 이름을 해쉬태그로 사용하는 경향 - 북미의 경우 K팝을 접할 수 있는 미디어나 채널을 해쉬태그에 사용하는 경향이 높음
  77. 77. North_America (미국, 캐나다) N=896
  78. 78. South_America (멕시코 브라질 콜롬비아 페루) N=774
  79. 79. Europe (독일 영국 스페인 프랑스) N=812
  80. 80. 유럽의 사례  유럽의 트위터 K팝 네트워크 - K팝 콘서트가 열린 프랑스가 K팝 확산의 주요 허브로 기능 - 다음으로 영국, 독일, 스페인, 이탈리아 - 동구 유럽의 경우 트위터 상에서 K팝 관련 담 화가 매우 부족
  81. 81. 히스패닉의 사례  시계열적 자료 수집을 통해 히스패닉 문화권의 트위터 K팝 네트워크의 성 장과 변화를 확인
  82. 82. Japan 일본 N=1744
  83. 83. Rest of Asia 아시아 기타지역 N=874
  84. 84. Indonesia 인도네시아 N=1588
  85. 85. Future collaboration • Applying webometrics to study the diffusion of Chinese pop culture on Web 2.0 Reach me at hanpark@ynu.ac.kr Follow my work at www.hanpark.net/
  86. 86. http://bigdatasoc.blogspot.co.uk/

×