An Epistemic Dynamic Model for Tagging Systems

<is web> Information Systems & Semantic Web

University of Koblenz ▪ Landau, Germany

An Epistemic Dynamic Model for Tagging Systems

Klaas Dellschaft and Steffen Staab{klaasd, staab}@uni-koblenz.de

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ISWeb - Information Systems & Semantic Web

Klaas [email protected]

Hypertext 20082 of 24

Delicious Tagging Interface

How do users influence each other in tagging systems? Hypothesis: Tagging involves imitation of other users AND

selection of tags from background knowledge of users.

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Understanding Dynamics in Tagging Systems

Conceptualization

Own Background Knowledge

Shared terminology

Something else?User interface

Tagging Behavior

Joint Stochastic Model

Model of Own Background Knowledge

Model of Sharing

Model of User Interface Influence

Simulated Tagging Behavior

Co

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n o

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Folksonomies

Vertexes: Users, tags, resources Hyperedges: Tag assignments (user X tag X resource) Postings:

Tag assignments of a user to a single resource Can be ordered according to their time-stamp

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Co-occurrence Streams

Co-occurrence Streams: All tags co-occurring with a given tag in a posting Ordered by posting time

Example tag assignments for ‘ajax': {mackz, r1, {ajax, javascript}, 13:25} {klaasd, r2, {ajax, rss, web2.0}, 13:26} {mackz, r2, {ajax, php, javascript}, 13:27}

Resulting co-occurrence stream:

Tag |Y| |U| |T| |R|ajax 2.949.614 88.526 41.898 71.525blog 6.098.471 158.578 186.043 557.017xml 974.866 44.326 31.998 61.843

javascript rss web2.0 php javascript

time

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Co-occurrence Streams – Tag Growth

Linear scaling of axes

Logarithmic scaling of axes

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Co-occurrence Streams – Tag Frequencies


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Resource Streams

Resource Streams: All tags assigned to a resource Ordered by posting time

Example tag assignments: {mackz, r1, {ajax, javascript}, 13:25} {klaasd, r2, {ajax, rss, web2.0}, 13:26} {mackz, r2, {ajax, php, javascript}, 13:27}

Resulting resource stream:

ajax rss web2.0 ajax php javascripttime

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Resource Streams – Tag Frequencies


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Resource Streams – Tag Frequencies

Taken from Halpin, Robu and Shepard, WWW 2007


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Existing Models

Frequency Rank

Co-occur. Streams Resource Streams Tag Growth

Polya Urn Model O O Fixed size

Simon Model O O Linear

YS Model w/ Memory + O Linear

Halpin et al. Model O O Linear

Observed Properties Long tail w/ cut-off Drop around tag 7 Sublinear

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Stochastic Models of Influence

Conceptualization


Shared terminology


Tagging Behavior



Model of Sharing



Co

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Modeling Tag Imitation

Delicious Tagging Interface Imitation of previous tag assignments Free input of new tags (taken from background knowledge of a user)

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Simulating a Tag Stream

p(w|t): Probability of selecting word w for topic t. Modeled by word distributions in a topic centered text corpus.

n: Number of visible previous tags.

h: Maximal number of previous tag assignments used for determining ranking of the n distinct tags.

P BK

1-PBK

Start with empty tag stream Each simulation step appends a new tag assignment:

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Modeling Background Knowledge

PBK: Probability of selecting from background knowledge p(w|t): Probability of selecting word w for topic t. Modeled by

word distributions in a topic centered text corpus. p(w|r): Probability of selecting word w for resource r.

Text Corpora Del.icio.usText Corpora

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Modeling Tag Imitation

PI = 1 - PBK: Probability of imitating a previous tag assignment n: Number of visible top-ranked tags h: Maximal number of previous tag assignments used for determining

ranking of the n distinct tags

PBK t t-1 t-2 t-3 t-4 t-5 … …t-h

1-PBK

1 2 3 … n

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Simulation Results

Conceptualization


Shared terminology


Tagging Behavior



Model of Sharing



Co

mp

ariso

n o

f Sta

tistics

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Simulating Co-occurrence Streams

Tag growth: Influenced by PBK and p(w|t)

Tag Frequencies: Influenced by PBK, p(w|t), n, h n: Semantic breadth of a topic (blog: 100 tags,

ajax: 50 tags, xml: 50 tags; Cattuto et al. 2007) h: No hint for realistic values. Good guess may be a value

around 1000.

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Co-occ. Streams – Simulated Tag Growth


PI PBKPI: Imitation

PBK: Background Knowl.

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Co-occ. Streams – Simulated Tag Frequencies


PI PBK

PI PBK

PI PBK

PI: Imitation


n: Visible Tags

h: Available History

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Simulating Ajax, Blog and XML

blog

ajax

xml


ObservationSimulation

(PI

(PI

(PI

PI: Imitation


n: Visible Tags


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Res. Streams – Simulated Tag Frequencies


PI PBK

PI PBK

PI: Imitation


n: Visible Tags


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Conclusions

• Imitation AND background knowledge are needed for explaining properties of tag streams

• Probability of imitating previous tag assignments: ~60-90%

Frequency Rank

Co-occur. Streams Resource Streams Tag Growth

Polya Urn Model O O Fixed size

Simon Model O O Linear

YS Model w/ Memory + O Linear

Halpin et al. Model O O Linear

Epistemic Model + + Sublinear

Observed Properties Long tail w/ cut-off Drop around tag 7 Sublinear

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Data sets and simulation software:http://isweb.uni-koblenz.de/Research/Tagdataset

http://www.tagora-project.eu

An Epistemic Dynamic Model for Tagging Systems

Education

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