Towards Understanding the Motivation Behind Tagging
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Towards Understanding the Motivation Behind Tagging

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  • Motivation for anything comes from within , it's never from an outward force. Like emotions come from inside out and not outside in. You see what I mean ? Anyway for now I'm motivated to share a beautiful like with you , right here!
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    Towards Understanding the Motivation Behind Tagging Towards Understanding the Motivation Behind Tagging Presentation Transcript

    • TU Graz – Knowledge Management Institute Towards Understanding the Motivation Behind Tagging Christian Körner tion p resenta y dele @Men Knowledge Management Institute Ph D ss Progre Graz University of Technology WorkIn 10 4Feb20 tagging n tio motiva todo nt Christian Körner Graz, February 4th, 2011 importa 1Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Outline of Todays Talk • Introduction • Motivation • Research Questions • Related Work • What happened so far? • Two Different Types of Tagging Motivation • Expected Contribution • Outlook Christian Körner Graz, February 4th, 2011 2Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Introduction / 1 •Tagging is an easy and intuitive way to annotate resources •A lot of current web platforms enable the tagging of resources •Tags: – are simple strings – add additional metadata to a resource – support re-finding of resources – enable the browsing of a user’s resource collection – mostly do not follow a controlled vocabulary How and which tags are applied to a resource depends on the user! Christian Körner Graz, February 4th, 2011 3Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Introduction / 2 Examples of Social Tagging Systems Christian Körner Graz, February 4th, 2011 4Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Introduction / 3 Resulting structure of social tagging systems consists of: – Users – Tags – Resources Folksonomy (all users of a system) Personomy (one user of a system) Christian Körner Graz, February 4th, 2011 5Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Motivation Getting a closer look at the motivation users of tagging systems have Inferring which users/tags are good for certain tasks: – searching in these systems – ontology learning – classification Improve tag recommendation engines Simulation of users and folksonomies Christian Körner Graz, February 4th, 2011 6Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Research Questions Is it possible to measure tagging motivation automatically? How do different motivations influence and transform resulting folksonomies? Based on these findings: – Can we improve existing mechanisms (such as tag recommendation)? – Is it possible to simulate whole folksonomies? Christian Körner Graz, February 4th, 2011 7Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Related Work (excerpt) [Golder2006] - studies folksonomies as a whole, shows stable patterns. Present a dynamic model of collaborative tagging. [Nov2009] - different motivations in an online photo sharing system: enjoyment, commitment, self development, reputation [Heckner2009] - studied resource sharing vs. personal information management in social tagging systems and propose model of information behavior in social tagging systems But all previous work relies on expert judgement! Christian Körner Graz, February 4th, 2011 8Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute What happened so far? Identification of two types of tagging motivation (two others are in the pipeline as well) Developed measures to detect the behavior Showed how tagging motivation can influence the resulting tags of a folksonomy and ontology learning[Körner2010a] Evaluated measures to identify the best for the differentiation [Körner2010b] Christian Körner Graz, February 4th, 2011 9Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Two Different Extreme Types of Tagging Motivation (so far) Categorizers Describers Christian Körner Graz, February 4th, 2011 10Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Categorizers - Using Tags for Categorization • Main focus: using tags for mimicking a taxonomy created by their personal preferences • they utilize tags so that their resources can be browsed more easily later • avoid synonyms • use limited tagging vocabulary • use “subjective” tags Christian Körner Graz, February 4th, 2011 11Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Describers - Using Tags to Describe Resources • Main focus: describing resources as detailed as possible • support search with their usage of tags • tagging vocabulary can contain synonyms • have an open tagging vocabulary • use “objective” vocabulary Christian Körner Graz, February 4th, 2011 12Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute (Current) Detection Measures Agnostic to semantics of used language Evaluate user behavior of single user (as opposed to the complete folksonomy) – no comparison to complete folksonomy necessary Inspect the usage of tags NOT their semantics: – How often are tags used? – How good does a user “encode” her resources with tags? – How many tags are used to annotate a single resource – etc. Christian Körner Graz, February 4th, 2011 13Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Results • An early stage of this work was presented at the ACM SRC Hypertext 2009 conference and won the 1st prize • Cooperation with KDE Kassel which resulted in a publication at the WWW2010 • One of the results of this work is that tagging pragmatics has impact on the semantic structure within a folksonomy • In essence: “Describers are better for the semantics within a tagging system” • Evaluation which measures perform best to differ types • Hypertext 2010 Christian Körner Graz, February 4th, 2011 14Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Some additional papers Examining which measures are better for measuring the generality of tags – Evaluate different folksonomy based measures with the help four different grounding sets • Currently under review at ESWC Identifying the impact of user behavior on automated classification – Automatically categorizing books into LCC and Dewey Classification Scheme – Categorizers are also good for something! – Although they use not that many words which are found in the descriptive data, they perform better with regard to classification. • Currently under review at HT2011 Christian Körner Graz, February 4th, 2011 15Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Expected Contribution Getting a closer look at the reasons why users tag Improve recommendation engines Enhancing search Enhancement of automated ontology learning Possible identification of spammers Christian Körner Graz, February 4th, 2011 16Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Possible Outlook Examine how tag recommendation can profit from knowledge of user motivation Investigate additional types of tagging motivation Using social network analysis for further investigation Using identified types of tagging motivation to build simulators Start writing the thesis Christian Körner Graz, February 4th, 2011 17Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Conclusion • Insight into my research on motivation behind tagging • Quick introduction about tagging • Motivation & Research Questions • Related Work • Categorizer VS. Describers • Work which was done so far • Expected Contribution & Outlook Christian Körner Graz, February 4th, 2011 18Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute Thank You For Your Attention Please feel free to ask questions! Christian Körner Graz, February 4th, 2011 19Friday, February 11, 2011
    • TU Graz – Knowledge Management Institute References [Ames2007] Ames, M. & Naaman, M. (2007), Why we tag: motivations for annotation in mobile and online media, in ‘CHI ’07’: Proceedings of the SIGCHI conference on Human factors in computing systems’ ACM, New York, NY, USA, pp.971--980 [Golder2006] Golder S. & Huberman B.; Usage Patterns of Collaborative Tagging Systems; Journal of Information Science; 32(2):198, 2006 [Heckner2009] Heckner, M; Heilemann, M. & Wolff, C. (2009) Personal Information Management vs. Resource Sharing: Towards a Model of Information Behavior in Social Tagging Systems, in ‘Int’l AAAI Conference on Weblogs and Social Media (ICWSM)’. [Körner2010a] Körner, C.; Benz, D.; Strohmaier, M.; Hotho, A. & Stumme, G. (2010), Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity, in Proceedings of the 19th International World Wide Web Conference (WWW 2010), ACM, Raleigh, NC, USA. [Körner2010b] Körner, C.; Kern, R.; Grahsl, H. P. & Strohmaier, M. (2010), Of Categorizers and Describers: An Evaluation of Quantitative Measures for Tagging Motivation, in 21st ACM SIGWEB Conference on Hypertext and Hypermedia (HT 2010), ACM, Toronto, Canada. [Nov2009] Nov, O.; Naaman, M. & Ye, C. (2010), Analysis of participation in an online photo- sharing community: A multidimensional perspective., JASIST 61(3), 555-566. Christian Körner Graz, February 4th, 2011 20Friday, February 11, 2011