Towards Understanding the Motivation Behind Tagging
This event took place on Wednesday 30 March 2011 at 11:30
Christian Körner Graz University of Technology
While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they influence the properties of resulting tags and folksonomies. My work tries to analyze the aims users have when they use such systems. To this purpose I introduce two vastly different types of tagging motivation and corresponding measures and detection measures. Subsequently I give an overview of implications on the structure of social tagging systems. Finally I sketch other types of tagging motivation and illustrate potential methods to differentiate them.
This event took place on Wednesday 30 March 2011 at 11:30
While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they influence the properties of resulting tags and folksonomies. My work tries to analyze the aims users have when they use such systems. To this purpose I introduce two vastly different types of tagging motivation and corresponding measures and detection measures. Subsequently I give an overview of implications on the structure of social tagging systems. Finally I sketch other types of tagging motivation and illustrate potential methods to differentiate them.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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