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
Asian Workshop of
Social Web and Interoperability
ASWC 2009 Dec. 7th , Shanghai, China
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
• 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
History
• First Mover
– Classmates.com, Match.com and sixdegree.com
– Friendster and Orkut
• Majority
– Myspace – Facebook – Linkedin – Twitter
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
Social Semantic Information Spaces
FOAF
Ontology describing persons, their activities and their relations to other people and objects.
SIOC
(John Breslin)Ontology interconnecting discussion methods such as blogs, forums and mailing lists to each other
10
11
FOAF+SIOC+SKOS
skos:isSubjectOf
sioc:topic
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
Online Presence Project
(Milan Stankovic)• Feel of Presense
– Status Messages– Online Status (Busy, Available, Away…) – Current listening music, activities…
Activity Streams
(Chris Messina)• Lightweight simple Atom based syndication for user’s activities
• Widely supported by Facebook, MySpace etc. • Basic Format
SemSNA
(Guillaume Erétéo)Ontology describing social network analysis notion such as centrality, degree and betweenness within users
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
What’s real problems?
– 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.
Remained Question in real world?
If you
’
re not Twitter, you cannot do anything.
1. What’s definition of Online Friend?
Online Friend != Real
FOAF’s knows is not knowing!
Well-known Friends 9% Colleagues 7% Meet once in offline 25% Knowing only name 12% Famous person 3% Unknown friend of friends 13%
Everyone who requests 32%
Known
Unknown
2. What’s meaning of online interaction?
Online Interaction != Real
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
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-interactionResults : me2day
Numbers Kinds of interaction Sharing items in SNS 3,590 GiftShort message by phone
30,000 SMS
Similar with Direct Messages
31,915 Private Messages
Similar with Retweets
451,260 Metoo
Comments between users
2,074,284 Reply
Result: Twitter
• Surveyed by total 1,120 Twitter users in Korea
– Reply interaction is growing along with followers.– ReTweet and Direct Message are less than reply
1 10 100 1000 10000 10 100 1000 10000 Reply ReTw eet DM Total Messages Total Followers
Suggestion: Interaction Index
• If the interaction index is “1”, it’s general
relationship.
• Ratio compared with interaction index between
user A and B is strength of betweenness.
Comparing with Reply
1.0000 2,591,049 Total 577.79 0.0014 3,590 Gift 69.14 0.0116 30,000 SMS 64.99 0.0123 31,915 Private Messages 4.60 0.1742 451,260 Metoo 1.00 0.8006 2,074,284 Reply Impact of Interaction Interaction Index Nb. Of Interaction
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
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)
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
• 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
