Modeling User Interactions in Online Social Networks to solve real problems
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1 Asian Workshop of Social Web and Interoperability ASWC 2009 Dec. 7 th, 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, , 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? Well-known Friends 9% Colleagues 7% Meet once in offline 25% Known 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. 12 th, 2009 Measuring differences of social interaction Classification of user-interaction Analysis of interaction statistics
31 Application: Twi2me
32 Results : me2day Kinds of interaction Reply Metoo Private Messages SMS Gift Numbers 2,074, ,260 31,915 30,000 3,590 Comments between users Similar with Retweets Similar with Direct Messages Short message by phone Sharing items in SNS
33 Poll survey of Direct Messages
34 Result: Twitter Total Messages Reply ReTw eet DM 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, Metoo 451, Private Messages 31, SMS 30, Gift 3, Total 2,591, 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 But, Metoo is easier than comment in me2day ReTweet is easier than direct message in Twitter 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
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