The wisdom in Tweetonomies
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The document discusses different types of social media streams and how their structures and emerging semantics are influenced by the type of stream aggregation. It presents a network-theoretic model of social awareness streams and analyzes hashtag streams, user list streams, and user directory streams on Twitter. Preliminary results show that hashtag streams demonstrate more robust latent conceptual structures than user list streams. Stream aggregation type affects both structural properties and emerging semantics.
![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] 26 April 2010](https://image.slidesharecdn.com/semsearchwagner-100426152139-phpapp01/85/The-wisdom-in-Tweetonomies-2-320.jpg)
![The advent of Tweetonomies? Taxonomy hand-crafted hierarchical structure of concepts for classification Folksonomy emerge when user collectively organize/classify resources conceptual structures and hierarchies on folksonomies (see e.g., [Schmitz, 2006], [Mika, 2007] and [Heymann, 2008]) Tweetonomy Do Tweetonomies emerge when users communicate and share information on SAS? To what extend does the type of stream aggregation and structure of stream aggregation influence emerging semantics? 26 April 2010](https://image.slidesharecdn.com/semsearchwagner-100426152139-phpapp01/85/The-wisdom-in-Tweetonomies-3-320.jpg)
![Network Transformations 26 April 2010 co-occurence context [ Harris , 1954] [Mika, 2007] communities](https://image.slidesharecdn.com/semsearchwagner-100426152139-phpapp01/85/The-wisdom-in-Tweetonomies-12-320.jpg)
![Thank you! 26 April 2010 http://clauwa.info/me [email_address] http://twitter.com/clauwa](https://image.slidesharecdn.com/semsearchwagner-100426152139-phpapp01/85/The-wisdom-in-Tweetonomies-18-320.jpg)
The wisdom in Tweetonomies
- 2. 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] 26 April 2010
- 3. The advent of Tweetonomies? Taxonomy hand-crafted hierarchical structure of concepts for classification Folksonomy emerge when user collectively organize/classify resources conceptual structures and hierarchies on folksonomies (see e.g., [Schmitz, 2006], [Mika, 2007] and [Heymann, 2008]) Tweetonomy Do Tweetonomies emerge when users communicate and share information on SAS? To what extend does the type of stream aggregation and structure of stream aggregation influence emerging semantics? 26 April 2010
- 4. Structure of SAS Users, messages and content of messages Content of messages: words, URLs, and other user-defined syntax such as hashtags, slashtags or @replies. Emerging collaboratively-defined syntax conventions make the structure of SAS more complex and dynamic than in other stream-based systems 26 April 2010
- 5. 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 26 April 2010
- 6. Example 26 April 2010
- 7. Experiment Aim Explore nature of different stream aggregation types Structure Structural stream measures Semantics Simple network transformations 26 April 2010
- 8. 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 26 April 2010
- 9. Structural Stream Measures (1) 26 April 2010
- 10. Structural Stream Measures (2) Social Diversity How many different users participate in a stream? Social variety: How balanced are their participations? Social balance: 26 April 2010
- 11. Experiment Aim Explore the nature of different stream aggregations Structure Structural stream measures Semantics Network-theoretic model of Social Awarness Streams 3-mode networks (users, resources and messages) Network transformations (projections) to obtain lower-order networks 26 April 2010
- 12. Network Transformations 26 April 2010 co-occurence context [ Harris , 1954] [Mika, 2007] communities
- 13. First Results (1) Type of stream aggregations influence stream structures Hashtags streams seem to be more informational than user list streams Hashtag streams seem to be more social diverse than user list streams User list streams seem to be slighly more conversational than hashtag streams 26 April 2010
- 14. First Results (2) Type of stream aggregations influence emerging semantics Hashtag stream aggregations are more robust against external disturbances than user list streams 26 April 2010 Hashtag Stream O R (RU a )S(R h ) User List Stream O R (RU a )S(R UL )
- 15. First Results (3) Type of network transformation influence emerging semantics Hashtags seem to be good context indicators Resource-hashtag networks reveal good latent conceptual structures 26 April 2010
- 16. Conclusion Theoretical Contribution Network-theoretic model of SAS Structural Stream Measures Empirical Study Do Tweetonomies emerge when users communicate and share information on SAS? Yes, latent conceptual structures can be observed Does the type of stream aggregation and structure of stream aggregation influence emerging semantics? Yes, stream aggregation type influences structural properties and emerging semantics 26 April 2010
- 17. References Z. Harris. Distributional structure. The Structure of Language: Readings in the philosophy of language,10:146-162, 1954. P. Heymann, G. Koutrika, and H. Garcia-Molina. Can social bookmarking improve web search? In WSDM '08: Proceedings of the international conference on Web search and web data mining, pages 195-206,New York, NY, USA, 2008. P. Mika. Ontologies are us: A unified model of social networks and semantics. Web Semant., 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. P. Schmitz. Inducing ontology from Fickr tags. In Proceedings of the Workshop on Collaborative Tagging at WWW2006, Edinburgh, Scotland, May 2006. 26 April 2010
- 18. Thank you! 26 April 2010 http://clauwa.info/me [email_address] http://twitter.com/clauwa