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

Delicious Tagging Interface <is web>

 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.

ISWeb - Information Systems Klaas Dellschaft Hypertext 2008
& Semantic Web klaasd@uni-koblenz.de 2 of 24

Understanding Dynamics in Tagging Systems <is web>
User interface Something else?

Tagging
Conceptualization Behavior

Comparison
Shared

of Statistics
Own Background
Knowledge terminology

Model of User Interface Influence

Simulated
Joint Stochastic Model Tagging
Behavior

Model of Own Model of Sharing
Background Knowledge

Folksonomies <is web>
 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


Co-occurrence Streams <is web>
 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:
javascript rss web2.0 php javascript
time

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

Co-occurrence Streams – Tag Growth <is web>

Linear scaling of axes

Logarithmic scaling of axes


Co-occurrence Streams – Tag Frequencies <is web>



Resource Streams <is web>
 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 javascript
time


Resource Streams – Tag Frequencies <is web>



Resource Streams – Tag Frequencies <is web>


Taken from Halpin, Robu and Shepard, WWW 2007

Existing Models <is web>

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


Stochastic Models of Influence <is web>

Tagging

Comparison
Shared

of Statistics
Own Background


Simulated
Behavior


Modeling Tag Imitation <is web>

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


Simulating a Tag Stream <is web>
 Start with empty tag stream
 Each simulation step appends a new tag assignment:

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

1-
P
BK

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


Modeling Background Knowledge <is web>

Text Corpora Del.icio.us
Text Corpora

 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.

Modeling Tag Imitation <is web>

PBK
t t-1 t-2 t-3 t-4 t-5 … t-h …

1-PBK

1 2 3 … n

 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


Simulation Results <is web>

Tagging

Comparison
Shared

of Statistics
Own Background


Simulated
Behavior


Simulating Co-occurrence Streams <is web>
 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.


Co-occ. Streams – Simulated Tag Growth <is web>

PI: Imitation PI PBK

PBK: Background Knowl.



Co-occ. Streams – Simulated Tag Frequencies <is web>

PI: Imitation
PI PBK
PBK: Background Knowl. PI PBK
PI PBK

n: Visible Tags

h: Available History



Simulating Ajax, Blog and XML <is web>

PI: Imitation (PI

(PI
(PI
n: Visible Tags


blog

ajax
Observation
xml
Simulation


Res. Streams – Simulated Tag Frequencies <is web>

PI: Imitation
PI PBK
PI PBK

n: Visible Tags




Conclusions <is web>
• 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


<is web>

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

http://www.tagora-project.eu


An Epistemic Dynamic Model for Tagging Systems

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