Semantic Imitation in Social Tagging

1. Semantic Imitation in Social Tagging Wai-Tat Fu, Thomas Kannampallil, Ruogu Kang, and Jibo He University of Illinois at Urbana-Champaign presented by Vasanth Pappu

2. Motivation Social tagging systems use open-ended tags links papers, books, blogs, etc... Anybody can tag anything Problem?

3. Motivation Vocabulary problem: diverse tags diverse information goals many tags for the same target does information retrieval become more difficult?

4. Examples information goals Traveler’s Books. A cruise line is starting a new reading program aboard one of its ships and is asking for your help in deciding what books to add to their library. The company has requested books that it believes vacationers would enjoy reading by the pool or on the beach and perhaps promote socialization in small groups. Please recommend a well-balanced set of books that you feel would be useful for the cruise line. Software Company. A mid-sized software company is looking to update their library to be used by all its employees, which include technical staff such as programmers and engineers, as well as nontechnical staff such as accountants, managerial staff, and employees in the marketing, purchasing and personnel departments. Please recommend a well-balanced set of books that you think would be useful and helpful in this library. does information retrieval become more difficult?

5. Examples information goals Local Arts Center. A local community is trying to promote art, design, and architecture and is building a new art center. They are looking for books that would be appropriate and interesting in the library of the new art center. Please select a well-balanced set of books that you would recommend the art center add to its library. Career Center. A career center is creating a library for its patrons and is requesting your help in picking out a set of books. The career center sees a variety of people, from college graduates to the recently unemployed to people looking to switch careers. Please select a list of books that you would recommend the career center add to its library. does information retrieval become more difficult?

6. Background semantic interpretation thread pin eye sewing sharp

7. Background semantic interpretation point pricked thimble injection

8. Background semantic interpretation haystack pain hurt

9. Motivation observations: organization in tags helpful in information goals frequency of tags is relatively constant stable usage patterns... why?

10. Motivation emergent patterns in social tags social influence model del.icio.us stable usage patterns... why?

11. Motivation patterns sensitive to semantics different words are related semantically similar to human communication stabilization: information goals of users social influence of tags other variables?

12. Motivation understanding tagging patterns information retrieval learning

13. Key Idea People’s tagging behavior is influenced by other peoples’ tagging behavior before them People tend to semantically imitate people before them

14. Social Tagging Social tagging systems allow users to annotate, categorize, and share Web content (links, papers, books, blogs, etc...) using short textual labels called tags

15. Some popular social tagging sites deli.cous Bibsonomy Flickr CiteULike Facebook etc...

17. The Approach what’s different semantics in social tagging behavior? previous studies: word-level includes semantic analysis

18. The Approach what’s different try to explain behavior at individual laboratory experiment vs massive statistics manipulated information goals control group can’t see tags of others novel analysis: connectedness tag co-occurrences semantic metrics

19. Background semantic interpretation book?

20. Background semantic interpretation car?

21. Background semantic interpretation needle?

22. Background semantic interpretation needle? memory illusion

23. Background semantic interpretation semantic representation of information major defining characteristics of human information processing

24. The Model semantic imitation model of social tagging how do people tag?

25. Concepts

26. The Model testable predictions users who can see tags by others users who can’t see tags will converge in tag choice may create tags that diverge less semantically similar tags more semantically similar may create tags that diverge effects of social tags and information goals will influence tag choices, but: social tags information goals

27. The Experiment Social group Nominal group Can only see their own tags, and no one else’s tags • Able to view previous tags by previous users What both groups did: they were instructed to perform searches, and to tag their resources that they look at. Sample task: A cruise line is starting a new reading program aboard one of its ships and is asking for your help in deciding what books to add to their library. The company has requested books that it believes vacationers would enjoy reading by the pool or on the beach and perhaps promote socialization in small groups. Please recommend a well-balanced set of books that you feel would be useful for the cruise line.

28. The Experiment

29. Results: Network Analysis Social group Nominal group Number of tags: 852 Average node degree: 18.9 Clustering Coefficient: 0.76 • Number of tags: 703 • Average node degree: 14.1 • Clustering Coefficient: 0.86 The network generated by the nominal group was more distributed and less connected than the social group

30. Analytical Methods network analysis of graph representations Latent Semantic Analysis (LSA)

31. Graph Representation (U,T, R)

32. Graph Representation

33. Graphs nodes edges tags cooccurrence on a resource standardized calculations node degree clustering coefficient

34. Graphs standardized calculations number of edges that are connected to that node a measure of the connectedness of a network node degree clustering coefficient

35. Graphs standardized calculations number of edges that are connected to that node a measure of the connectedness of a network node degree clustering coefficient word – level, but what about semantic- level?

36. Graphs word – level, but what about semantic- level? intertag cooccurrence index (CI) a similarity measure

37. Semantic Relatedness of Tags LSA (Latent Semantic Analysis) a measure of similarity of meaning of words analysis of large bodies of text http://lsa.colorado.edu

38. Semantic Relatedness of Tags CI – tag choices + LSA – semantic relatedness extent of association

39. The Experiment venue: CiteULike www.citeulike.com a research literature sharing website

42. CITE U LIKE TAG CLOUDS EXAMPLE HERE

43. CiteULike actions add resources (links to paper, etc...) create tags search for related resources tag cloud search keywords search resources with other users find resources browsing through resource library using keywords for search queries select tags from tag cloud select tags associated with library resources

44. The Experiment 150 books 8 categories, equal number of books instructions: search for books of a certain topic information goals

45. The Experiment task: recommend books ideal for rehabilitation center task: recommend books ideal for daycare search: health care, medication, children, school, etc... different information goals save useful books assign tags to each book selected

46. The Experiment 64 participants 32 control group 32 social group 4 sessions, 8 in each session 4 sessions, 8 in each session one unique topic per participant avoid randomness: initial neutral tags added to books (3 tags)

47. Results: Network Analysis Social group Nominal group Number of tags: 852 Average node degree: 18.9 Clustering Coefficient: 0.76 • Number of tags: 703 • Average node degree: 14.1 • Clustering Coefficient: 0.86 The network generated by the nominal group was more distributed and less connected than the social group

48. Results convergence in the social group

49. Results network and node analysis of two groups were similar representative of large-scale social tagging networks

50. Results semantic relatedness