Asian Workshop for Social Web and Interoperability on ASWC 2009

1. Asian Workshop of ASWC 2009
Social Web and Interoperability Dec. 7th , Shanghai, China
Modeling User Interactions in
Online Social Networks
to solve real problems
Seokchan (Channy) Yun and Hong-Gee Kim
Biomedical Knowledge Engineering Laboratory
Seoul National University, Korea

2. Agenda
• Introduction
– Some approaches for Social Semantic Web
• Challenges
– Finding the definition of online friends and interaction
between users
• Survey of social interaction in real SNS
– Twitter and Me2day
• Result and Discussion
• Conclusion and Future plan

3. Emerging Online Social Network

4. • New opportunities for social science
– Explicit and implicit social network information
– Large scale and dynamic data sets
– Different modalities (profiles, email, IM, Twitter…)
• Challenges
– Friend on the Web = Friend in reality?
– Heterogeneity and quality of data
– Time and space complexity
– Ethical and legal challenges
– Complex interaction = Centrality in reality?

5. History
• First Mover
– Classmates.com,
Match.com and
sixdegree.com
– Friendster and Orkut
• Majority
– Myspace
– Facebook
– Linkedin
– Twitter

6. How succeed?
• Allows a user to create and maintain an online network of
close friends or business associates for social and
professional reasons:
– Friendships and relationships
– Offline meetings
– Curiosity about others
– Business opportunities
– Job hunting
• Allows a user to share interests based on object-centered
sociality with meaning
– Sharing photo, video and bookmark
– Life streaming over SNS
– Broadcasting and publishing of my own content

7. Social Semantic Information Spaces
John Breslin, The Social Semantic Web: An Introduction (2009)

8. FOAF
Ontology describing persons, their activities and their relations
to other people and objects.

9. SIOC (John Breslin)
Ontology interconnecting discussion methods such as blogs,
forums and mailing lists to each other

10. FOAF+ SIOC
10

11. FOAF+SIOC+SKOS
skos:isSubjectOf
sioc:topic
11

12. Tripartite Social Ontology (Peter Mica)
• A graph model of ontologies based on tripartite
graphs of actors, concepts and instances
– Actors: users
– Concepts: tags
– Instances: objects
• Emergent semantics
– General idea: observe semantics in the way agents interact
(use concepts)
• Bottom-up ontologies
• Semantics = syntax + statistics

13. Online Presence Project (Milan Stankovic)
• Feel of Presense
– Status Messages
– Online Status (Busy, Available, Away…)
– Current listening music, activities…

14. Activity Streams (Chris Messina)
• Lightweight simple Atom based syndication for user’s
activities
• Widely supported by Facebook, MySpace etc.
• Basic Format
– User, Verb, Noun

15. SemSNA (Guillaume Erétéo)
Ontology describing social network analysis notion such as
centrality, degree and betweenness within users

16. SemSIO = SIOC+SemSNA (Guillaume Erétéo, ISWC2009)

17. Limitations
• FOAF
– Only focusing on ONE PERSON
• SIOC
– Only focusing on relationship with site (forum), contents and person.
• Tripartite Social Ontology
– Too high abstraction level to be implemented
• Online Presence Project
– Only focusing “Presence” not to be interested in “Activity
• Activity streams
– Only description for Person / Verb / Object
• SemSNI
– Only can be applied in specific domain if you have all data

18. What’s real problems?
• Twitter
– There are many spammers and followers.
– Whom I should follow? Who is expert?
• me2DAY (or Facebook)
– There are many friends
– Who disconnected in my friendship?
• Flickr
– There are many photos.
– What’s good photos enjoying with friend?
• RateMDs
– There are many doctors.
– What’s good doctors recommended by friends?

19. Remained Question in real world?
If you’re not Twitter, you cannot do anything.
How about semantically dealing with real social web?

20. 1. What’s definition of Online Friend?
http://answers.polldaddy.com/poll/1230119/?view=results
Well-known Friends 9%
Colleagues 7%
Known
Meet once in offline 25%
Knowing only name 12%
Famous person 3%
Unknown friend of friends 13% Unknown
Everyone who requests 32%
Online Friend != Real
FOAF’s knows is not knowing!

21. Twitter

22. Facebook

23. me2DAY

24. LinkedIn

25. 2. What’s meaning of online interaction?
Online Interaction != Real
SemSNA’s centrality is not real!

26. Facebook interaction

27. Twitter interaction

28. me2DAY interaction

29. Challenges
• Online friends and interaction are not real
because there are no limits of time and space.
• It’s hard to find degree of user relationship.
– Coupling-decoupling between users (high vs. weak) by
time change
• We have to consider the difference of each online
interaction to measure proper centrality and
betweenness.

30. Approach
• Sample data analysis of Me2day and Twitter
– Developing Twitter application: Twi2me
• Twi2me helps for user to post Tweets to me2day in real-time.
– Me2day: gathering interaction on purpose of research of
32,200 accounts from January to October, 2009
– Twitter: gathering interaction 1,120 users on time of Oct.
12th , 2009
• Measuring differences of social interaction
– Classification of user-interaction
– Analysis of interaction statistics

31. Application: Twi2me

32. Results : me2day
Kinds of interaction Numbers
Reply 2,074,284 Comments between users
Metoo 451,260 Similar with Retweets
Private Messages 31,915 Similar with Direct Messages
SMS 30,000 Short message by phone
Gift 3,590 Sharing items in SNS

33. Poll survey of Direct Messages

34. Result: Twitter
Total Messages
10000
1000
Reply
100
ReTw eet
10 DM
1
10 100 1000 10000
Total Followers
• Surveyed by total 1,120 Twitter users in Korea
– Reply interaction is growing along with followers.
– ReTweet and Direct Message are less than reply

35. Suggestion: Interaction Index
Nb. Of Interaction Interaction Index Impact of Interaction
Reply 2,074,284 0.8006 1.00
Metoo 451,260 0.1742 4.60
Private Messages 31,915 0.0123 64.99
SMS 30,000 0.0116 69.14
Gift 3,590 0.0014 577.79
Total 2,591,049 1.0000 Comparing with Reply
• If the interaction index is “1”, it’s general
relationship.
• Ratio compared with interaction index between
user A and B is strength of betweenness.

36. Discussion
• Q: Interaction depends on user experience?
– User tends to do easy interactive method.
– ReTweet is harder than reply in Twitter.
• A: User does emotional interaction.
– For example, agreement and consensus
• Metoo is easier than comment in me2day
• ReTweet is easier than direct message in Twitter
– But,
• Nb. of comment > Nb. Of metoo
• Nb. of direct message == Nb. of ReTweet (Information distribution)

37. Conclusion
• Difference of strength in user interaction
– Twitter:
• Reply < ReTweet < Direct Message < SMS
– me2Day
• Comment < metoo < Private Messages < SMS < Gift
• Measuring strength of user relationship
– Modeling of user degree
– Measuring Interaction Impact
– Similarity formula (A,B)
• Solving problem after integration data

38. Future Plan
• Social web evolves direct sharing and
broadcasting instead of document link based
distribution and knowledge discovering.
– Social Interaction is more important in social networks.
– FriendFeed, Facebook life streaming, Twitter
• Need to represent “Degree between people”
– Writing simple ontology represents interaction
• Channy replies Hong-Gee (What) (When) in Facebook
• John retweets Channy (What) (When) in Twitter
– Extending ActiveStreams or SemSNI

39. • Who disconnected in my friendship on me2DAY?
– Gathering me2day activities
– Measuring interaction factor and coupling degree
• Distance = # of interaction/ time interval
• Priority = normalized value for each interactions
– Evaluation with user’s reaction for alert
• “Why don’t you contact this person because it’s long time not to contact
by you?”
• Whom I should follow? Who is expert in Twitter?
– Gathering twitter activities
– Measuring interaction factor and coupling-degree
– Evaluation with user’s reaction for recommendation

40. Q&A
channy@snu.ac.kr
http://www.creation.net
Twitter: @channyun