The document analyzes usage patterns of collaborative tagging systems based on data from Delicious.com. It finds that (1) while user activity levels varied, tagging quantities did not strongly correlate with bookmark quantities, (2) new tags emerged as users refined their sensemaking over time, (3) tags fell into categories like topics, types, or qualities, with basic-level tags used most frequently, (4) bookmarks experienced bursts of initial popularity driven by external factors before stabilizing, and (5) tag proportions within bookmarks stabilized into consistent patterns influenced by shared knowledge and social imitation across users.

1. Usage patterns of
collaborative tagging
systems
+ Journal of Information
Science 2006
-Scott A. Golder and
Bernardo A. Huberman
/김혁진
x 2015 Spring

2. Journal of Information Science (JIS)
Published 6 times a year 8~10 Articles
February 1979 - April 2015
2013 Impact Factor : 1.087
Ranking :
Information Science & Library Science 29 out of 84 Computer
Science, Information Systems 65 out of 135

3. Journal of Information Science Article lists
1 A linguistic approach for determining the topics of Spanish Twitter messages
2 Hybrid string matching algorithm with a pivot
3 Is seeking health information online different from seeking general information online?
4 On methods and tools of table detection, extraction and annotation in PDF documents
5 Graphically structured icons for knowledge tagging
6 Towards improving XML search by using structure clustering technique
7 Modelling liking networks in an online healthcare community: An exponential random
graph model analysis approach
8 Automatic Arabic text categorization: A comprehensive comparative study
9 A continuous rating model for news recommendation
10 Text messaging and retrieval techniques for a mobile health information system
11 Accurate similarity index based on the contributions of paths and end nodes for link
prediction
12 Building and evaluating a collaboratively built structured folksonomy
13 A social inverted index for social-tagging-based information retrieval
14 Keyword-based mobile semantic search using mobile ontology
15 Folksonomy-based user interest and disinterest profiling for improved recommendations:
An ontological approach

4. Journal of Information Science Article lists
1 A linguistic approach for determining the topics of Spanish Twitter messages
2 Hybrid string matching algorithm with a pivot
3 Is seeking health information online different from seeking general information online?
4 On methods and tools of table detection, extraction and annotation in PDF documents
5 A research case study for user-centred information literacy instruction: information behaviour of translation trainees
6 Towards improving XML search by using structure clustering technique
7 Modelling liking networks in an online healthcare community: An exponential random graph model analysis
approach
8 Automatic Arabic text categorization: A comprehensive comparative study
9 A continuous rating model for news recommendation
10 Text messaging and retrieval techniques for a mobile health information system
11 Accurate similarity index based on the contributions of paths and end nodes for link prediction
12 Building and evaluating a collaboratively built structured folksonomy
13 Graphically structured icons for knowledge tagging
14 Keyword-based mobile semantic search using mobile ontology
15 Folksonomy-based user interest and disinterest profiling for improved recommendations: An ontological approach
• information seeking behaviors

5. Journal of Information Science Article lists
1 A linguistic approach for determining the topics of Spanish Twitter messages
2 Hybrid string matching algorithm with a pivot
3 Is seeking health information online different from seeking general information online?
4 On methods and tools of table detection, extraction and annotation in PDF documents
5 A research case study for user-centred information literacy instruction: information behaviour of translation trainees
6 Towards improving XML search by using structure clustering technique
7 Modelling liking networks in an online healthcare community: An exponential random graph model analysis
approach
8 Automatic Arabic text categorization: A comprehensive comparative study
9 A continuous rating model for news recommendation
10 Text messaging and retrieval techniques for a mobile health information system
11 Accurate similarity index based on the contributions of paths and end nodes for link prediction
12 Building and evaluating a collaboratively built structured folksonomy
13 Graphically structured icons for knowledge tagging
14 Keyword-based mobile semantic search using mobile ontology
15 Folksonomy-based user interest and disinterest profiling for improved recommendations: An ontological approach
• information literacy and information education

6. Coverage of Journal of Information Science
Any aspect of information science theory, policy, application, practice
• information seeking behaviors
• information literacy and information education
• information flow and communication
• knowledge structuring and organization
• search, navigation and retrieval techniques
• information processing and management

7. Usage patterns of
collaborative tagging
systems
+ Journal of Information
Science 2006
-Scott A. Golder and
Bernardo A. Huberman
/김혁진
x 2015 Spring

8. Index
1. Abstract
2. Background
3. 3 Related concepts
4. Method
5. 6 Findings
6. Conclusion
Usage patterns of collaborative tagging systems

9. Abstract
Key words : collaborative tagging, folksonomy, delicious.com, bookmarks, web, sharing
Collaborative tagging
• Structure ?
• Tagging Regularity, Frequency, Kinds?
• Bursts of popularity in bookmark ?
• Tagging proportion Stable?

10. Background
Tag : 나중에 navigation, filtering, search를 위한 contents 붂류
과거 : 사서(authority) -> 요즘 : 사용자 (collaborative tagging)
Delicious.com
Yahoo’s MyWeb CiteULike Connotea
Collaborative tagging = folksonomy ⊂ 분류체계
folks+order+nomes
대중들(folks)의 명령에 (order) 따라 이름(nomes)을 붙임

11. Related concept 1. Tagging vs Taxonomy
TaggingTaxonomy
• Non-hierarchical
• Inclusive
• Hierarchical
• Exclusive
Examples
• 린네의 생물 붂류법
• 듀이 십진 도서 붂류법
• 컴퓨터 폴더 구조
Q : 아프리카 사는
고양이과 동물
#치타
아닌 것을 걸러내기정확한 위치를 찾아가기
Examples
• Keyword-based search

12. Related concept 2. Tagging 시스템의 문제점 3
1. Polysemous : 다의성
Word that has many related senses. E.g) window
c.f)homonymy (동음이의어)는 추가적으로 붙은 태그로 식별가능
2. Synonymy: 동의성
Multiple words having the same or closely related meanings. E.g) television , TV
3. Basic level variation: 기본 수준 범주
General Specific
#animal #dog #beagle
Basic level = dog (응답까지 걸리는 시갂이 가장 짧음)
Factor of Basic level variation : degree that makes difference in the lives
E.g) 지식수준, 사화적 문화적 붂류기준

13. Related concept 3. Tagging
Tagging is fundamentally about sensemaking
정보를 붂류하고 이름 붙이는 과정에서 의미가 형성 (1)
Basic level : 해당 level의 사물과 사람이 interact하는 방식 (2)
-> Tag : 바깥세상의 사물을 붂류해서 의미를 찾는 과정
(1)K. Weick, K. Sutcliffe and D. Obstfeld, Organizing and the process of sensemaking, Organizational Science 16(4) (2005) 409–21.
(2)W. Labov, The boundaries of words and their meanings. In C.J. Bailey. New Ways of Analyzing Variation in English (1973) 340–73.
자싞의 경험, 일상의 일,
필요, 관심을 반영
사회적 영향 (문화, 지역별),
축적된 공동체 지식,
(power struggle) Collective tagging
General
meaning
Personal
meaning

14. Methods : Delicious dynamics
-소셜 북마크 관리
5일갂의 2개의 data set
1st set : 실험기갂 동안 popular에 나왔던 모든 URL 212개
각각에 대한 모든 북마크(실험기갂 외 포함) 19422개
2nd set : 실험기갂 동안 액티브한 229 User
그 유저들이 만든 북마크(실험기갂 외 포함) 68668개

15. 1. User activity and tag quantity
Tag를 많이 사용하기도 / 적게 사용하기도..
Bookmarks 개수 – no strong relationship – tag 개수
(n=229, R2 = 0.33)
Delicious.com을 많이 사용하기도 / 적게 사용하기도..
가입기갂 – weak relationship – 하나이상 Bookmark 생성
(n=229, R2 = 0.52)

16. 2. Emerging new Tag
1. 새로운 관심 ‘tag’발견
2. 더 적합한 ‘tag’를 찾지 못해서
Sensemaking은 회고(retrospective)하는 과정이므로
새로운 Tag를 달기 젂까지는 중요한지도 계속 모름
Filtering하는데 어려움 (이젂정보에 태그추가 힘듦)

17. 3. Kinds of tag.
1. Identifying what or who it is about - topic
2. Identifying what kind – article, blog, book
3. Identifying who owns it
4. Refining categories – not stand alone 수치정보%
5. Identifying qualities or characteristics – scary funny
6. Self reference – mystuff mycomment
7. Task organizing – toread jobsearch
General
meaning
Personal
meaning

18. 4. 1st Tag is basic level
먼저 쓰는 Tag = 가장 많이 사용되는 Tag = Basic level
(Basic level은 가장 빨리 말해진다는 실험과 연관 있음)
Fig. 5. As a tag’s order in a bookmark (horizontal) increases, its rank
(i.e. frequency) in the list of tags (vertical) decreases. This pattern is shown here for two URLs (#1209 and #1310).

19. 5. Outbursting bookmark
처음 popular해지는 것은 외부요인으로 인해 폭발적으로 증가
‘Popular’ 페이지가 있어서 popular한 것은 유지되는 속성이 있음
142(67%) reached their peak popularity in their first 10 days
37(17%) were in the system for six months before reached peak
37(17%) on the first day

20. 6. Stable patterns in tag proportion
하나의 URL에 대해서 Tag들의 비율이 stable한 패턴을 보임
(경험적으로 Bookmark 100번 젂에 안정성이 나타남)
Dynamics of a stochastic urn model – fixed but random model by initial state
->개인의 tagging data를 모으는 것은 의미 있음
근거1. imitation – 다른 유저 따라 하기 (같은 색의 공을 추가 하는 것)
social proof : 남이 선택한 것이 옳다고 생각함
근거2. shared knowledge – 공개되지 않은 tag들도 안정된 패턴 보임
안정화된 shared knowledge가 있음

21. Conclusion
• 안정된 Tag가 웹문서가 어떻게 상호작용하고 구성되는지를 설명
• Collaborative tag는 다양하다 (Tag개수, 빈도, 종류)
• Bookmark는 외부요인으로 폭발적으로 증가한 뒤 유지됨
• 안정된 패턴이 있으며 이것은 여러 유저들로부터 만들어 진다
• 개인적인 사용을 위해 만든 Tag도 다른 유저에게 도움이 됨 (#funny)
• Collaborative tagging system을 추천 시스템으로 사용할 수 있음
Structure ?
Tagging Regularity, Frequency, Kinds?
Bursts of popularity in bookmark ?
Tagging proportion Stable?