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Collective Emotions in Cyberspace

Projective objectives

and summary of main results

in the second project period1 Feb.2010 -31. Jan. 2011

In the name of CYBEREMOTIONS Consortium

Janusz Hołyst, Project Coordinator, Warsaw University of Technology, jholyst@if.pw.edu.pl

www.cyberemotions.eu

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Collective Emotions in CyberspaceEuropean Union Research Project (FP7 FET)

Participant organisation name Leaders Country SpecializationWarsaw University of Technology Janusz Hołyst Poland Physics of complex systems Ecole Polytechnique Fédérale de Lausanne 

Daniel Thalmann Stephane Gobron

Switzerland Virtual reality 

University of Wolverhampton  Michael Thelwall United Kingdom WebometricsÖsterreichische Studiengesellschaft für Kybernetik

Robert Trappl Marcin Skowron 

Austria Human‐computer interactions

ETH Zürich  Frank Schweitzer David Garcia

Switzerland Chair of  systems design

Jozef Stefan Institute, Ljubljana  Bosiljka Tadic Slovenia Physics of complex networksJacobs University, Bremen  Arvid Kappas Germany PsychophysiologyTechnical University Berlin Matthias Trier Germany Dynamic network analysisGemius SA  Anna Borowiec Poland Online  research agency

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€

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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. ….

Twitter revolution

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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. 

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1. Developing and performing EmoChatting experimentswhere 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

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

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Purpose of WP2 Emotionally reacting active agents (EPFL)

• 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

Graph

ics &

 Emotion

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A first CG emotional model

“Hello,nice day!”

Data miningClassifier

WP2

WP2

+

{v,a}selection

WP2

WP32010_wp2_firstYearSummary_v2.mov

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VR‐server/clientsCG animations

‐ Facial and body emotional real‐time animated interpretations

A second CG emotional model

e.g. of dialog

A: “Hello”

A: “What? ”

B: “Hi chick!”

Multi classifiers- “SuperClassifier”- “ANEW”

B: “sorry…”

A: “It’s ok ”

Emotional model- {v,a,d}- Target- Polarity

Dynamic event manager- Multi user- Free interaction

VH emotional mind, eg.:- 3D emo. model {v,a,d}- Memory based on

history of dialogs

WP2

WP2

WP2WP5

WP3

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Result: towards a virtual social environmentincluding verbal and non‐verbal communication 

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EmoChattingDialog system - Virtual Bartender

DS <-> WOZ

WP4: acquired data‐setsAu

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EmoDialogOnline Virtual Bartender

Role of system’s affective profile

21 IRC channelsSame time-frame

variety of channels & discussed topics

2009-2010Recent “hot topics” of discussion:politics, economy, social issues

2200 days of cooperationLong-term online communications

between members of Ubuntu communities

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Dialog System vs. Wizard of Oz setting- emotional connection, dialog realism

Affect Listener ‐ Evaluation of Affective Dialog Systems

Aust

rian

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Effect of affective profile in the interaction with a dialog system

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Virtual Reality settingsAu

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• 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 differencesin 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) avalanches distribution observed in BBC blogs and Digg data by JSI;(ii) 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.

WP6/JSI: Quantitative Analysis of User's Collective Behaviors

M. Mitrović, G. Paltoglou and B. Tadić, JSTAT (2011) P02005

Mapping the data on bipartite networks:Communities on the networks (Eigenvalue spectral analysis).

Time-series analysis and avalanches of positive/negative comments.

Distribution of sizes of emotional avalanches Indicates Self-Organized Criticality in the dynamics.

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

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 networkFixed 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.

M. Mitrović and B. Tadić, DRAFT (2011)

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.

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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).

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.

WP1, Warsaw

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Growth probability for cluster of size n

neepneep )|()|(

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 length.

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Demonstrator CMXViewer (D 8.2)

Workpackage 8 (TUB)

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

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Sentiment network visualisation (TU Berlin) 

22DIGG discussion network (Discussion ID 11223766) rendered with the CMXViewer (TUB)

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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

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Sentiment Triad Census AnalysisEmotional persitence of IRC chatts 

Emotional clustersEmotional avalanches

Hurst eponents

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Sentiment Networks EvolutionIRC channel interaction network 

(IRC channel "edubuntu_2007_11")

Movies\movie1.wmvMovies\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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#oscars% m

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Increase in negative sentiment strength

Date and time

Date and time

9 Feb 2010

9 Feb 2010

9 Mar 2010

9 Mar 2010

An analysis of Twitter sentiment around the top 30 mediaevents showed that increases in interest were typicallyassociated with increases in negative sentiment, even

for positive events – such as the oscars

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.

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EMG(smiling, frowning)

EKG(heart rate)

EDA(sweating)

Continuous recording of psychophysiology during participation in a forum discussion

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EKG (heart rate)

Sample of recording output with a selection of channels

EMG(smiling, frowning)

Digital online event identifier (“marker”)

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SummaryNew 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