2015-02-25 research seminal, paul seitlinger
TRANSCRIPT
Marks of Stabilizing Patterns on the Web Blessing or Curse for Individual Thought…
Austrian Science Fund
omefispo
P 27709-G22
Overcoming Mental Fixation by switching the internal Spotlight
Memory retrieval in tagging: A Socio-Cognitive Model
Paul Seitlinger 25 February 2015
Understanding Behavior of Collectives
Individuals and their intentions
Non-human actants (e.g., artifacts) introduced to manifest intentions
How the web of relations between human and non-human actants evolves in time (Law, 2009)
Organism environment coupling (Järvilehto, 1998)
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Organism Environment Coupling
2 empirical examples and simulation-based analyses
Stabilization in interpreting and tagging Web resources
Evolution of information needs in searching for Web resources
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Example of stabilization
Quick non-linear stabilization in tagging objects in Delicious
Difference between every two points in time of the tag distribution for a given resource
Kullback-Leibler Divergence KLD
KLD reaches a value of 0 after only a few time points
Result triggered several studies to develop models of semantic stabilization
Halpin et al. (2007; similar results found by Golder & Huberman, 2007; Wagner et al., 2014)
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Models of stabilization
Artifact-mediated feedback cycles
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Resource Semantic field
Tag choice
Tag recommendations (tx, ty, tz)
y
z
x1
2
Future interpretations
Semantic imitation: Tags of previous users shape the interpretation of the resource (Tag-based priming; Fu, 2008; Fu & Dong, 2012)
1. Tag-based priming 2. Implicit learning
2
Implicit learning: Long-term effects of other users’ tags (Seitlinger & Ley, 2011; Seitlinger, Ley & Albert, 2015)
romanticthreatening
Simulating non-linear stabilization by implementing implicit reinforcement learning in connectionist networks
(Ley, Seitlinger & Pata, in press)
Is the mechanism of implicitly learning associations between perceived tags and semantic fields/clusters sufficient to explain stabilization?
Multi-Agent simulation
Each agent is a connectionist network (Hutchins & Hazlehurst, 1995; Overwalle & Heylighen 2006)
Learns to categorize and tag objects
Objects are taken from a large-scale data set of Delicious bookmarks of Wikipedia articles (Arkaitz et al., 2013)
Articles described by Wikipedia categories
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Simulating non-linear stabilization by implementing implicit reinforcement learning in connectionist networks
(Ley, Seitlinger & Pata, in press)
• SUSTAIN-Network (Love et al., 2004) spreads activation across 3 layers to choose a set of tags TAS probabilistically (numbers 1-3)
• Network can “see” MPT = the 7 most popular tags at a given point in time (number 4)
• Output-Encoding (Rizzuto & Kahana, 2002): Associations wjk for tags included in TAS and MPT are strengthened = Artifact-mediated output encoding (number 5)
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Simulating non-linear stabilization by implementing implicit reinforcement learning in connectionist networks
(Ley, Seitlinger & Pata, in press)
• M=50 agents, N=300 unique objects, 100 simulation runs
• Training phase
• 50 users randomly sampled from a large-scale data set from Delicious (Arkaitz et al., 2013)
• Each user’s history to train each of the M agents -> Non-random behavior at the beginning
• Two measures of stabilization during “communication” phase
• 1) Similarity of TAS and MPT: Jaccard Index J
• 2) Kullback-Leibler Divergence KLD
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Simulating non-linear stabilization by implementing implicit reinforcement learning in connectionist networks
(Ley, Seitlinger & Pata, in press)
• Results
0.00
0.05
0.10
0.15
Consecutive agent tagging events ATE
Sim
ilarit
y be
twee
n TA
S an
d M
PT
6 65 124 182 241 300
J(TAS,MPT)
0.0
0.2
0.4
0.6
0.8
1.0
Consecutive object tagging events OTE
Kullb
ack−
Leib
ler D
iverg
ence
3 12 22 31 41 50
KLD
• A high degree of randomness changes quickly into a stable state
• Artifact-mediated output encoding as an organism-environment coupling causing stabilization
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Semantic Stabilization – A Conclusion
Social cues influence interpretations and descriptions
Disambiguation (Tag-based priming)
Implicit reinforcement learning
Automatic processes result in homogeneous categorization patterns
Do the influenced interpretations in turn affect our information needs and hence, search patterns?
A further stabilizing mechanism?
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DNMs (e.g., Polyn et al., 2009) to account for a non-linear evolution of information needs
Cues of a Web resource, such as tags, activate a pattern fi on a feature layer F
fi illuminates a pattern of activation tIN on a latent topic layer T via MFT (neural network)
tIN is integrated into already illuminated pattern ti-1 (previous information need)
ti then actualizes the feature pattern on F through
fIN = ti MTF
fIN might be applied to form a new search cue and trigger the retrieval of a new Web resource.
In each cycle of this environment organism coupling, MFT and MTF are adjusted through Hebbian learning of new associations between fi and tIN.
T
F
“Neural” network representation of tag topic associations/1611
Proximal cuesResource features F
Latent topicsInformation need T
MFTMTF Searching the Web
fi
tINti
fIN
ti = �ti-1 + �tIN
DNMs to account for non-linear organism-environment coupling
Asymmetric search patterns in Delicious
A tag of a current bookmark fi is more likely to occur in a subsequent fi+1 than in a preceding bookmark fi-1
Interpretation of current bookmark tIN on T might influence fIN which drives search for new bookmark fi+1
DNM-based search simulation also produces an asymmetric similarity curve
Might capture cue-dependent memory processes involved in searching the Web
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−20 −10 0 10 20
0.00
0.10
0.20
0.30
Distance from last bookmark collected
Sim
ilarit
y of
tag
assi
gnm
ents
−20 −10 0 10 20
0.00
0.10
0.20
0.30
Distance from last bookmark collected
Sim
ilarit
y of
tag
assi
gnm
ents
Empirical
Simulation
f1 f2 fi-1 fi fi+1 fi+2 f20
……..tx, ty tx, tztu, tv
Summary
Examples of organism environment coupling
Environmental cues influence mental states
they make people learn similar interpretations
The influenced states determine future behavior
the influenced interpretations cause a drift in information needs
What might be a consequence for collaboration within a social information system?
Homogeneous interpretation of different objects
Homogeneous search patterns
Consensual state of minds
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Virtual Echo Rooms
Current semantic technologies create “virtual echo rooms”
Complicate the emergence and retrieval of novel ideas
Make us circulate around ourselves by searching for familiar things
Slavoj Zizek
• Concerns are justified particularly in social media applications for political deliberation (e.g., Stieglitz & Xuan, 2012)
• Echo rooms counteract a multi-discoursive ordering (Law, 2009)
• Homogeneity accelerates polarization (Huckfeldt et al., 2005)
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Questions we have to consider in the context of Collective Awareness Platforms for Sustainability
CAPS
How can both collaboration and deliberation within a social information system take place?
What are alternatives to current aggregating technologies based on frequency, recency and similarity?
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