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1 TU Graz – Knowledge Management Institute Graz, June 9 th , 2010 Towards Understanding the Motivation Behind Tagging Christian Körner Knowledge Management Institute Graz University of Technology

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Page 1: Towards Understanding the Motivation Behind Taggingmarkusstrohmaier.info/courses/SS2010/707.000_web-science/... · 2012. 7. 10. · [Nov2009] - different motivations in an online

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

Towards Understanding the Motivation Behind Tagging

Christian Körner

Knowledge Management Institute Graz University of Technology

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

Outline of Todays Talk

•  Introduction • Motivation •  Research Questions •  Related Work • What happened so far? •  Two Different Types of Tagging Motivation •  Expected Contribution • Outlook

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

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!

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

Introduction / 2

Examples of Social Tagging Systems

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

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)

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

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

Improve tag recommendation engines

Simulation of users and folksonomies

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

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?

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

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!

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

What happened so far?

Identification of two types of tagging motivation

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]

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

Two Different Extreme Types of Tagging Motivation (so far)

Categorizers

Describers

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

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

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

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

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

(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.

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

Examples of Current Detection Measures

Conditional Tag Entropy

Orphan Ratio

Tags per Resource tpr = |T| / |R|

Tags per Post tpp = |TAS|/|R|

Vocabulary Size vocab = |V|

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

What we found out / 1

Tagging motivation varies within and across tagging systems

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

What we found out / 2

Tagging motivation can change over time.

Human Subject Study verified that the measures indicate the tagging behavior

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

What we found out / 3

Users who are motivated by by description agree on more tags

–  Tag agreement among Delicious users for 500 most popular resources

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

Results

•  Cooperation with KDE Kassel which resulted in a publication at the WWW2010 - more on that later

• Evaluation which measures perform best to differ types - Hypertext 2010

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

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

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

Outlook / Next Steps

Finish the PhD proposal

In-depth look into recommender selection (in cooperation with KDE Kassel)

Investigate additional types of tagging motivation

Using social network analysis for further investigation

Using identified types of tagging motivation to build simulators

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

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

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TU Graz – Knowledge Management Institute

Graz, June 9th, 2010

Thank You For Your Attention

Please feel free to ask questions!

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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.

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Christian Körner1, Dominik Benz2, Andreas Hotho3, Markus Strohmaier1, Gerd Stumme2

Stop thinking, start tagging: Tag Semantics arise from Collaborative Verbosity

1Knowledge Management Institute and Know Center,

Graz University of Technology, Austria

2Knowledge and Data Engineering Group (KDE),

University of Kassel, Germany

3Data Mining and Information Retrieval Group

University of Würzburg, Germany

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30.04.2010 Körner, Benz et al.: Tag Semantics arise from Collaborative Verbosity @ WWW2010 2 / 20

Where do Semantics come from?

  Semantically annotated content is the „fuel“ of the next generation World Wide Web – but where is the petrol station?

  Expert-built expensive

  Evidence for emergent semantics in Web2.0 data Built by the crowd!

Which factors influence emergence of semantics?

Do certain users contribute more than others?

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The Story

Emergent Tag Semantics

Pragmatics of tagging

Semantic Implications of Tagging Pragmatics

Conclusions

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30.04.2010 Körner, Benz et al.: Tag Semantics arise from Collaborative Verbosity @ WWW2010 4 / 20

Emergent Tag Semantics

  tagging is a simple and intuitive way to organize all kinds of resources

  uncontrolled vocabulary, tags are „just strings“

  formal model: folksonomy F = (U, T, R, Y)   Users U, Tags T, Resources R

  Tag assignments Y ⊆ (U×T×R)

  evidence of emergent semantics   Tag similarity measures can

identify e.g. synonym tags (web2.0, web_two)

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Tag Similarity Measures: Tag Context Similarity

  Tag Context Similarity is a scalable and precise tag similarity measure [Cattuto2008,Markines2009]:   Describe each tag as a context vector   Each dimension of the vector space correspond to

another tag; entry denotes co-occurrence count   Compute similar tags by cosine similarity

5 30 1 10 50

design software blog web programming

… JAVA

Will be used as indicator of emergent semantics!

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= tag

Assessing the Quality of Tag Semantics

JCN(t,tsim) = 3.68 TagCont(t,tsim) = 0.74

Folksonomy Tags = synset

WordNet Hierarchy

Mapping

Average JCN(t,tsim) over all tags t: „Quality of semantics“

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30.04.2010 Körner, Benz et al.: Tag Semantics arise from Collaborative Verbosity @ WWW2010 7 / 20

The Story

Pragmatics of tagging

Semantic Implications of Tagging Pragmatics

Conclusions

Tag Similarity measures can capture emergent tag semantics

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30.04.2010 Körner, Benz et al.: Tag Semantics arise from Collaborative Verbosity @ WWW2010 8 / 20

Tagging motivation

  Evidence of different ways HOW users tag (Tagging Pragmatics)   Broad distinction by tagging motivation [Strohmaier2009]:

donuts

duff

marge beer

bart

barty

Duff-beer

bev

alc nalc

beer wine

„Categorizers“…

-  use a small controlled tag vocabulary

-  goal: „ontology-like“ categorization by tags, for later browsing

-  tags a replacement for folders

„Describers“…

-  tag „verbously“ with freely chosen words

-  vocabulary not necessarily consistent (synomyms, spelling variants, …)

-  goal: describe content, ease retrieval

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Tagging Pragmatics: Measures

  How to disinguish between two types of taggers?   Intuition: Describers use open set of many tags,

Categorizers use small set of controlled tags:

  Vocabulary size:

  Tag / Resource ratio:

  Average # tags per post:

high

low

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Tagging Pragmatics: Measures

  Next Intuition: Describers don‘t care about „abandoned“ tags, Categorizers do

  Orphan ratio:

  R(t): set of resources tagged by user u with tag t

high

low

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Tagging pragmatics: Limitations of measures

  Real users: no „perfect“ Categorizers / Describers, but „mixed“ behaviour

  Possibly influenced by user interfaces / recommenders

  Measures are correlated

  But: independent of semantics; measures capture usage patterns

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The Story

Semantic Implications of Tagging Pragmatics

Conclusions

Tag Similarity measures can capture emergent tag semantics

Measures of tagging pragmatics differentiate users by tagging motivation

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Influence of Tagging Pragmatics on Emergent Semantics

  Idea: Can we learn the same (or even better) semantics from the folksonomy induced by a subset of describers / categorizers?

Extreme Categorizers

Extreme Describers

Complete folksonomy

Subset of 30% categorizers

= user

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Experimental setup

1.  Apply pragmatic measures vocab, trr, tpp, orphan to each user 2.  Systematically create „sub-folksonomies“ CFi / DFi by

subsequently adding i % of Categorizers / Describers (i = 1,2,…,25,30,…,100)

3.  Compute similar tags based on each subset (TagContext Sim.) 4.  Assess (semantic) quality of similar tags by avg. JCN distance

TagCont(t,tsim)= …

JCN(t,tsim)= …

DF20 CF5

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Dataset

  From Social Bookmarking Site Delicious in 2006 ORIGINAL   Two filtering steps (to make measures more meaningful):

  Restrict to top 10.000 tags FULL   Keep only users with > 100 resources MIN100RES

dataset |T| |U| |R| |Y|

ORIGINAL 2,454,546 667,128 18,782,132 140,333,714

FULL 10,000 511,348 14,567,465 117,319,016

MIN100RES 9,944 100,363 12,125,176 96,298,409

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Results – adding Describers (DFi)

Almost all sub-folksonomies are better than random-picked ones

40% of describers according to trr outperform complete data!

Optimal performance for 70% describers (trr)

more describers

bett

er s

eman

tics

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Results – adding Categorizers (CFi)

Almost all sub-folksonomies are worse than random-picked ones

Global optimum for 90% categorizers (tpp) removing 10% most extreme describers! (Spammers?)

bett

er s

eman

tics

more categorizers

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The Story

Tag Similarity measures can capture emergent tag semantics

Measures of tagging pragmatics differentiate users by tagging motivation

Sub-folksonomies introduced by measures of pragmatics show different semantic qualities

Conclusions

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Summary & Conclusions

  Introduction of measures of users‘ tagging motivation (Categorizers vs. Describers)

  Evidence for causal link between tagging pragmatics (HOW people use tags) and tag semantics (WHAT tags mean)

  „Mass matters“ for „wisdom of the crowd“, but composition of crowd makes a difference („Verbosity“ of describers in general better, but with a limitation)

  Relevant for tag recommendation and ontology learning algorithms

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Guess who‘s a Categorizer from the authors

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Thanks for the attention! Questions? Be verbous

Tag Similarity measures can capture emergent tag semantics

Measures of tagging pragmatics differentiate users by tagging motivation

Sub-folksonomies introduced by measures of pragmatics show different semantic qualities

Evidende of causal link between pragmatics and semantics of tagging!

[email protected] [email protected]

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References

  [Cattuto2008] Ciro Cattuto, Dominik Benz, Andreas Hotho, Gerd Stumme: Semantic Grounding of Tag Relatedness in Social Bookmarking Systems. In: Proc. 7th Intl. Semantic Web Conference (2008), p. 615-631

  [Markines2009] Benjamin Markines, Ciro Cattuto, Filippo Menczer, Dominik Benz, Andreas Hotho, Gerd Stumme: Evaluating Similarity Measures for Emergent Semantics of Social Tagging. In: Proc. 18th Intl. World Wide Web Conference (2009), p.641-641

  [Strohmaier2009] Markus Strohmaier, Christian Körner, Roman Kern: Why do users tag? Detecting users‘ motivation for tagging in social tagging systems. Technical Report, Knowledge Management Institute – Graz University of Technology (2009)