modeling user interactions in online social networks (2009)
DESCRIPTION
Asian Workshop for Social Web and Interoperability on ASWC 2009TRANSCRIPT
Modeling User Interactions in Online Social Networks
to solve real problems
Seokchan (Channy) Yun and Hong-Gee Kim
Biomedical Knowledge Engineering LaboratorySeoul National University, Korea
Asian Workshop ofSocial Web and Interoperability
ASWC 2009Dec. 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• Conclusion and Future plan
Emerging Online Social Network
• 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?
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– Broadcasting and publishing of my own content
Social Semantic Information Spaces
John Breslin, The Social Semantic Web: An Introduction (2009)
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
FOAF+ SIOC
11
FOAF+SIOC+SKOS
skos:isSubjectOfsioc: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
• Semantics = syntax + statistics
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
– User, Verb, Noun
SemSNA (Guillaume Erétéo)
Ontology describing social network analysis notion such as centrality, degree and betweenness within users
SemSIO = SIOC+SemSNA (Guillaume Erétéo, ISWC2009)
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
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?
Remained Question in real world?
If you’re not Twitter, you cannot do anything.How about semantically dealing with real social web?
1. What’s definition of Online Friend?
Online Friend != RealFOAF’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
http://answers.polldaddy.com/poll/1230119/?view=results
me2DAY
2. What’s meaning of online interaction?
Online Interaction != RealSemSNA’s centrality is not real!
Facebook interaction
Twitter interaction
me2DAY interaction
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.
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
Application: Twi2me
Results : me2dayNumbersKinds of interaction
Sharing items in SNS3,590Gift
Short message by phone30,000SMS
Similar with Direct Messages31,915Private Messages
Similar with Retweets451,260Metoo
Comments between users2,074,284Reply
Poll survey of Direct Messages
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 Reply1.00002,591,049Total
577.79 0.0014 3,590Gift
69.14 0.0116 30,000SMS
64.99 0.0123 31,915Private Messages
4.60 0.1742 451,260Metoo
1.00 0.8006 2,074,284ReplyImpact of InteractionInteraction IndexNb. 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
• Nb. of direct message == Nb. of ReTweet (Information distribution)
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
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
• 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
