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The Wisdom of the Tweets
Knowledge Acquisition from
Social Awareness Streams
Claudia Wagner [email protected]
a TRADITION of INNOVATION
(Supervisor: Markus Strohmaier)
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Social Awareness Streams (SAS)
Short, natural-language messages created by users
Broadcasted
Information consumption is driven by social networks
Applications such as Twitter or Facebook
[Naaman, 2010]
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What‘s the knowledge of SAS?
Users
Web Resources
Real-World Happenings
Natural Language Constructs
http://www.flickr.com/photos/matthewfield/2306001896/
What‘s the knowledge of SAS?
Users
Web Resources
Natural Language Constructs
Real-World Happenings
http://www.flickr.com/photos/waldoj/722508166/
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Proposed Approach
Aim What kind of knowledge is contained in SAS How we can acquire knowledge from SAS Which factors influence knowledge acquisition results
Method Develop a SAS Analyzer system Controlled experiments
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Research Questions
Do ontological structures emerge from SAS?
Which factors influence their emergence? Stream aggregation/sampling strategies? Semantic enrichment strategies? Knowledge acquisition methods and algorithms?
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SAS Analyzer
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Controlled Experiments (1)
Do ontological structures emerge from SAS?
ground truth ontologyrandomly sample
Input labels
Compare (e.g., via Precision, Recall, RLA [Maedche et al, 2000] )
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Controlled Experiments (2)
Which factors influence emerging semantics?
ground truth ontologyrandomly sample
Input labels
vary variables
Compare (e.g., via Precision, Recall, RLA [Maedche et al, 2000] )
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Preliminary Results
Network-theoretic Model of SAS
Structural Stream Measures
First Experiment on acquiring latent conceptual structures from SAS
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A network-theoretic model of SAS
A Social Awareness Stream is a tupel
U, M and R are finite sets whose elements are called users, messages and resources
q1, q2, q3 are qualifiers
Y is a ternary relation
ft is a function
fl is a function
),,,,,( 321 flftYRMUS qqq
),(: longlatYfl
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Example
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Structural Stream Measures (1)
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Structural Stream Measures (2)
Social Diversity How many different users participate in a stream? Social variety:
How balanced are their participations? Social balance:
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Experiment
Aim Can we observe emerging semantics from SAS?
Method Input: topic of interest, in our case „semanticweb“ 4 different stream aggregations 3-mode networks (users, resources and messages) Network transformations (projections) to obtain lower-
order networks of resources Output: weighted resource networks Manual evaluation
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Dataset
4 different stream aggregations from Twitter
Same topic Hashtag stream: #semanticweb Keyword stream: semanticweb and semweb User list stream: semweb user list from twitter user sclopit User directory stream: wefollow semanticweb directory
Same time interval 2 time intervals: 16th of Dec 2009 - 20th of Dec 2009 and
29th of Dec 2009 - 1st of Jan 2010
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Network Transformations
co-occurence
context
[Harris, 1954]
[Mika, 2007]
communities
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First Results (1)
Hashtag StreamOR(RUa)S(Rh)
User List StreamOR(RUa)S(RUL)
Type of stream aggregations influence emerging semantics Hashtag stream aggregations are more robust against
external disturbances than user list streams
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First Results (2)
Type of network transformation influence emerging semantics Hashtags seem to be good context indicators Resource-hashtag networks reveal good latent
conceptual structures
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Limitations
Small Dataset
Only one topic/domain
Manual Evaluation
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ReferencesZ. Harris. Distributional structure. The Structure of Language:
Readings in the philosophy of language,10:146-162, 1954.
A. Maedche and S. Staab. Discovering Conceptual Relations from Text. In: W.Horn (ed.): ECAI 2000. In Proceedings of the 14th European Conference on Artificial Intelligence, Berlin, Amsterdam, 2000.
P. Mika. Ontologies are us: A unified model of social networks and semantics. Web Semantics, 5(1):5-15, 2007.
M. Naaman, J. Boase, and C.-H. Lai. Is it all about me? user content in social awareness streams. In Proceedings of the ACM 2010 conference on Computer supported cooperative work, 2010.
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Thank you!
http://clauwa.info/me
http://twitter.com/clauwa