Modeling User Interactions in Online Social Networks (2009)
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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
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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
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Emerging Online Social Network
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• 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?
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History
• First Mover– Classmates.com,
Match.com and sixdegree.com
– Friendster and Orkut
• Majority– Myspace– Facebook– Linkedin– Twitter
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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
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Social Semantic Information Spaces
John Breslin, The Social Semantic Web: An Introduction (2009)
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FOAF
Ontology describing persons, their activities and their relations to other people and objects.
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SIOC (John Breslin)
Ontology interconnecting discussion methods such as blogs, forums and mailing lists to each other
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10
FOAF+ SIOC
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11
FOAF+SIOC+SKOS
skos:isSubjectOfsioc:topic
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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
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Online Presence Project (Milan Stankovic)
• Feel of Presense– Status Messages– Online Status (Busy, Available, Away…)– Current listening music, activities…
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Activity Streams (Chris Messina)
• Lightweight simple Atom based syndication for user’s activities
• Widely supported by Facebook, MySpace etc.• Basic Format
– User, Verb, Noun
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SemSNA (Guillaume Erétéo)
Ontology describing social network analysis notion such as centrality, degree and betweenness within users
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SemSIO = SIOC+SemSNA (Guillaume Erétéo, ISWC2009)
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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
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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?
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Remained Question in real world?
If you’re not Twitter, you cannot do anything.How about semantically dealing with real social web?
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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
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me2DAY
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2. What’s meaning of online interaction?
Online Interaction != RealSemSNA’s centrality is not real!
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Facebook interaction
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Twitter interaction
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me2DAY interaction
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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.
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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
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Application: Twi2me
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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
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Poll survey of Direct Messages
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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
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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
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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)
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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
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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
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• 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