slides - CyberEmotions

www.cyberemotions.eu
Collective Emotions in Cyberspace
Projective objectives
and summary of main results
in the second project period
1 Feb.2010 -31. Jan. 2011
In the name of CYBEREMOTIONS Consortium
Janusz Hołyst, Project Coordinator, Warsaw University of Technology,
[email protected]
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Collective Emotions in Cyberspace
European Union Research Project (FP7 FET)
Large-scale integrating project, ICT Call 3 Science of Complex Systems for Socially
Intelligent ICT. Duration: 1 Feb. 2009 - 31. Jan. 2013. EC funding 3.6 M€
Participant organisation name Leaders
Warsaw University of Technology Janusz Hołyst
Ecole Polytechnique Fédérale de Daniel Thalmann Lausanne Stephane Gobron
University of Wolverhampton Michael Thelwall
Österreichische Studiengesellschaft Robert Trappl Marcin Skowron für Kybernetik
Frank Schweitzer ETH Zürich David Garcia
Jozef Stefan Institute, Ljubljana Bosiljka Tadic
Jacobs University, Bremen Arvid Kappas
Technical University Berlin
Matthias Trier
Gemius SA Anna Borowiec
Country
Specialization
Poland
Switzerland
Physics of complex systems Virtual reality United Kingdom Webometrics
Austria
Human‐computer interactions
Switzerland
Chair of systems design
Slovenia
Germany
Germany
Poland
Physics of complex networks
Psychophysiology
Dynamic network analysis
Online research agency
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Twitter revolution
Egyptian Revolution:
Egyptian protest leader Wael
Ghonim’s Twitter message:
“congratulations Egypt the
criminal has left the palace.”
Egypt, Twitter and the Straw Man Revolution
….Twitter is not the root cause of these uprisings.
Twitter was not repressed. Twitter did not get inspired
by events in other countries. ….
Twitter can help organize. Facebook can help get the
word out. ….
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Main project tasks • automatic collection and classification of sentiment data in various e‐communities as well as cross‐
validation of such classifiers using psycho‐physiological methodologies, • qualitative and quantitative sentiment data analysis and data‐driven modeling of collective emotions by ABM, complex networks and fluctuation scaling paradigms,
• development of emotionally intelligent ICT tools such as affective dialog systems and graphically animated virtual agents that communicate by emotional interactions. 4
During the second year of its project life CYBEREMOTIONS proved its ambition
to be the leading world enterprise in the domain of affective interactions observed
in e-communities. CyberEmotions datasets were mentioned in a list of 'The 70
Online Databases that Define our Planet', posted by the Physics arXiv Blog,
published by MIT.
Main results from the second project period
1. Developing and performing EmoChatting experiments
where users had discussions in real time via their avatars with
other avatars or simulated agents. The experiments integrated a
sentence-based emotion generator and applied graphics engine
using models of valence, arousal and dominance for emotional
coordinates as well as asymmetric facial expressions. They
were carried out by EPFL in close collaboration with OFAI,
UW, ETH, WUT, and JUB groups.
2011_wp2_twoYearsSummary.mp4
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Purpose of WP2 Emotionally reacting active agents (EPFL)
Graphics & Emotion
• Computer graphical metaphor of emotion applied to Virtual Reality
WP specification
• Create a virtual society composed of VH, capable of reactions, emotions, and social behavior
• Develop interpersonal relationships and nonverbal communication in a virtual society
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A first CG emotional model
{v,a}
selection
“Hello,
nice day!”
WP2
WP2
Data mining
Classifier
WP3
WP2
+
2010_wp2_firstYearS
ummary_v2.mov
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A second CG emotional model
VR‐server/clients
WP5
WP2
Dynamic event manager
Emotional model
- Multi user
- {v,a,d}
- Free interaction
- Target
- Polarity
VH emotional mind, eg.: WP2
- 3D emo. model {v,a,d}
Multi classifiers
- Memory based on
- “SuperClassifier”
history of dialogs
- “ANEW”
WP3
e.g. of dialog
WP2
CG animations
‐ Facial and body emotional real‐
time animated interpretations
A: “Hello”
B: “Hi chick!”
A: “What? ”
B: “sorry…”
A: “It’s ok ”
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Result: towards a virtual social environment
including verbal and non‐verbal communication 9
Austrian Research Institute for Artificial Intelligence – OFAI
WP4: acquired data‐sets
EmoChatting
Dialog system - Virtual Bartender
DS <-> WOZ
EmoDialog
Online Virtual Bartender
Role of system’s affective profile
21 IRC channels
Same time-frame
variety of channels & discussed topics
2200 days of cooperation
Long-term online communications
between members of Ubuntu communities
2009-2010
Recent “hot topics” of discussion:
politics, economy, social issues
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Austrian Research Institute for Artificial Intelligence – OFAI
Affect Listener ‐ Evaluation of Affective Dialog Systems
Dialog System vs. Wizard of Oz setting
- emotional connection, dialog realism
Effect of affective profile in the interaction
with a dialog system
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Austrian Research Institute for Artificial Intelligence – OFAI
Virtual Reality settings
• Integration of Affect Listener dialog system (OFAI), virtual reality
event engine (EPFL) and sentiment classifier (UW)
• Comparison of dialog system with WOZ setting
• Dialog system results in pair with WOZ ratings
• correlation coefficient for DS and WOZ:
- chatting enjoyment
0.95
- emotional connection 0.96
- dialog realism
0.97
• no statistically significant differences
in the participants ratings
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2. Confirmation of the emergence of collective emotions in cyber‐communities by four project teams applying different methods and using independent datasets
(i)
(ii)
avalanches distribution observed in BBC blogs and Digg data by JSI;
non‐random clusters distribution observed in Blogs06, BBC Forum, Digg and IRC channels by WUT; (iii) persistent character of sentiment dynamics observed by ETHZ for IRC channels using the Hurst exponent analysis; (iv) causal sentiment triad distribution found in Network Motif Analysis
by TUB.
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WP6/JSI: Quantitative Analysis of User's
Collective Behaviors
Mapping the data on bipartite networks:
Communities on the networks
(Eigenvalue spectral analysis).
Time-series analysis and avalanches
of positive/negative comments.
Accurate mapping of high-resolution data onto
bipartite graphs: Users (bulits) and
Post&Comments (squares), direction of links
indicate user's actions; Color: Emotional content
Distribution of sizes of emotional
avalanches Indicates Self-Organized
Criticality in the dynamics.
M. Mitrović, G. Paltoglou and B. Tadić, JSTAT (2011) P02005
WP6/JSI: Agent‐Based Models on Networks
Emotional agents – 2D
emotional states (arousal,
valence);
Emotion dynamics on networks;
2 types of AB model on
Growing bipartite network
Fixed social network
Rules of actions and
parameters extracted from the
Data of Blogs and Diggs and
MySpace network.
Emotional state of each agent
depends his/hers connections on
the network.
Simulated time-series of charge expressed in
comments of 5 communities found on the
emergent network: g2 and g3 (with negative
charge) continue to grow.
M. Mitrović and B. Tadić, DRAFT (2011)
WP1, Warsaw
Classification based on standard supervised, machine-learning.
Emotions
e { +1, 0 , -1}
hierarchical extension - a document is initially classified by the algorithm as
objective or subjective and in the latter case a second-stage classification
determines its polarity, either positive or negative.
Example discussion
+1 0 -1 -1 -1 +1 0 0 +1 -1
We define an emotional cluster of size n as a chain of n consecutive messages
with similar sentiment orientations (i.e. negative, positive or neutral).
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Growth probability for cluster of size n
p (e | ne)  p (e | e)n

The presence of a longer
cluster of coherent
emotional expressions
increases a possibility to
follow the cluster by a
comment with the same
emotion.
Conditional probability for cluster growth increases as a power-law with cluster
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length.
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Demonstrator CMXViewer (D 8.2)
Development of Demonstrator
Software CMXViewer (D 8.2):
• Animated Graph Visualization
representing sentiment
propagation processes
• Dynamic visualization of
longitudinal network data in 2D
and 3D showing sentiment
dissemination processes
• Sentiment-based dynamic link
coloring
Workpackage 8
(TUB)
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Sentiment network visualisation (TU Berlin) 21
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DIGG discussion network (Discussion ID 11223766) rendered with the CMXViewer (TUB)
Central
component
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Negative
subnetwork
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Positive
subnetwork
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Sentiment Triad Census Analysis (D 8.3)
Workpackage 8
(TUB)
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Collective emotions of cybercommunities detected by various methods
Emotional avalanches
Emotional persitence of IRC chatts Hurst eponents
t
Emotional clusters
Sentiment Triad Census Analysis
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Sentiment Networks Evolution
IRC channel interaction network (IRC channel "edubuntu_2007_11")
Movies\movie1.wmv
Movies\movie2.wmv
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WP3 Summary (Wolverhampton) 1. SentiStrength algorithm evaluation and improvement‐ detects positive and negative sentiment strength in short informal text
2. Six human classified sentiment strength data sets with >1000 classifications (MySpace, Twitter, BBC, Digg,YouTube, RunnersWorld)
3. Ternary lexicon‐based classifier for social media: Twitter, MySpace, Digg
4. Ordinal prediction of valence and arousal on forum posts: LiveJournal
5. Real‐valued prediction on [1,9] scale of valence/arousal: BBC forum discussions
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% matching posts
An analysis of Twitter sentiment around the top 30 media
events showed that increases in interest were typically
associated with increases in negative sentiment, even
for positive events – such as the oscars
#oscars
Subj. Sentiment strength
9 Feb 2010
9 Feb 2010
Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of
the American Society for Information Science and Technology, 61(12), 2544–2558.
Date and time
9 Mar 2010
Increase in negative sentiment strength
Date and time
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9 Mar 2010
Continuous recording of psychophysiology during participation in a forum discussion
EMG
(smiling, frowning)
EKG
(heart rate)
EDA
(sweating)
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Sample of recording output with a selection of channels
EKG (heart rate)
EMG
(smiling, frowning)
Digital online event identifier
(“marker”)
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Summary
New data sets:
•
corpora of posts at Twitter and Newsgroups; YouTube
•
physiological responses after reading and writing emotional posts
Confirmation of collective character of emotions in various e‐communities:
•
BBC blogs and Digg (avalanches) •
Blogs06, BBC Forum, Digg and IRC channels by WUT (clustering)
•
IRC channels (Hurst exponents)
•
Digg (Motif Analysis)
Models of emotions dynamic in cyberecommunities •
Agent based models
•
Stochastic models EmoChatting and EmoDialog experiments
•
Based on multidimenional emotion models •
Applications of avatars or agents •
Role of assymetrical facial expressions
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Collaboration to other Projects and Programmes
• Coordination Action ASSYST
• Flagship initiative FuturIcT
• Flagships Midterm Conference, Warsaw,
November 2011
• Proposal Automatic Detection of Affective
Mails (ADAM) submitted to Swiss-Polish
Research Programme.
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More results will be presented at Partners presentations 36