User interaction-social media-100102032820-phpapp01
Modeling User Interactions inOnline Social Networksto solve real problemsSeokchan (Channy) Yun and Hong-Gee KimBiomedical Knowledge Engineering LaboratorySeoul National University, KoreaAsian Workshop ofSocial Web and InteroperabilityASWC 2009Dec. 7th , Shanghai, China
Agenda• Introduction– Some approaches for Social Semantic Web• Challenges– Finding the definition of online friends and interactionbetween users• Survey of social interaction in real SNS– Twitter and Me2day• Result and Discussion• Conclusion and Future plan
• 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 andsixdegree.com– Friendster and Orkut• Majority– Myspace– Facebook– Linkedin– Twitter
How succeed?• Allows a user to create and maintain an online network ofclose friends or business associates for social andprofessional reasons:– Friendships and relationships– Offline meetings– Curiosity about others– Business opportunities– Job hunting• Allows a user to share interests based on object-centeredsociality with meaning– Sharing photo, video and bookmark– Life streaming over SNS– Broadcasting and publishing of my own content
Social Semantic Information SpacesJohn Breslin, The Social Semantic Web: An Introduction (2009)
FOAFOntology describing persons, their activities and their relationsto other people and objects.
SIOC (John Breslin)Ontology interconnecting discussion methods such as blogs,forums and mailing lists to each other
Tripartite Social Ontology (Peter Mica)• A graph model of ontologies based on tripartitegraphs 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’sactivities• Widely supported by Facebook, MySpace etc.• Basic Format– User, Verb, Noun
SemSNA (Guillaume Erétéo)Ontology describing social network analysis notion such ascentrality, 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– 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%KnownUnknownhttp://answers.polldaddy.com/poll/1230119/?view=results
Challenges• Online friends and interaction are not realbecause there are no limits of time and space.• It’s hard to find degree of user relationship.– Coupling-decoupling between users (high vs. weak) bytime change• We have to consider the difference of each onlineinteraction to measure proper centrality andbetweenness.
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 of32,200 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
Results : me2dayNumbersKinds of interactionSharing items in SNS3,590GiftShort message by phone30,000SMSSimilar with Direct Messages31,915Private MessagesSimilar with Retweets451,260MetooComments between users2,074,284Reply
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 reply11010010001000010 100 1000 10000ReplyReTw eetDMTotal MessagesTotal Followers
Suggestion: Interaction Index• If the interaction index is “1”, it’s generalrelationship.• Ratio compared with interaction index betweenuser A and B is strength of betweenness.Comparing with Reply1.00002,591,049Total577.790.00143,590Gift69.140.011630,000SMS64.990.012331,915Private Messages4.600.1742451,260Metoo1.000.80062,074,284ReplyImpact 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 andbroadcasting instead of document link baseddistribution 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 contactby 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