Cluster2013

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  • 1. The dialog system was applied for the acquisition of supplementary data, that help to extend the scope of analysis in: - quantitative way – reaching to the users who do not voice their opinions in the online debates - qualitative way – allowing to conduct a follow-up dialogs, related to the introduced set of topics as well as user’s expressions of affective states 2. Two experimental settings were used for the dialog system evaluation: - Virtual Reality in which the dialog system managed the verbal aspects of communication between a Virtual Human (Virtual Bartender) and users Online web chat environment, typical for several internet communication interfaces 3. In the first interaction settings the system was compared with Wizard-of-Oz settings in terms of: establishing an emotional connection dialog realism providing an enjoyable chatting experience 4. 2 nd round of experiments focused on: - acquisition of data on user’s responses to the introduced topics of interests (announced tax increase, smoking prohibition in the public places, allegations of a bribery related with the organization of UEFA soccer championships) - assessing the effect of the system affective profile in the interaction with users -
  • Cluster2013

    1. 1. Collective Emotions in Cyberspace Short review of Cyberemotions project results In the name of CYBEREMOTIONS Consortium Janusz Hołyst, Project Coordinator, Warsaw University of Technology, jholyst@if.pw.edu.pl www.cyberemotions.eu
    2. 2. Plan • Emotions, cyberemotions, collective emotions and collective cyberemotions • Cyberemotions Project structure • Main results of various Project layers: - data collection and classification - collective character of cyberemotions and data driven models of cybercommunities - project ICT outputs
    3. 3. There is no agreement what emotions are but they are important for our life !
    4. 4. Negative stimuli negative emotion forces ACTION And they can be useful for fast reactions !!!
    5. 5. Collective effects do matter .... also for emotions !
    6. 6. Collective emotions
    7. 7. 5.09.2012 Satellite meeting at ECCS'12 Emotion in cyberspace ?
    8. 8. 8 Twitter Revolution Pic. Arab Spring in Egypt 2011 congratulations Egypt the criminal has left the palace – a tweet from Egyptian protest leader Wael Ghonim. Twitter can help organize. Facebook can help get the word out. YouTube provides evidence. Over the past few years, we've seen that social media can be a powerful tool in assisting revolutions in countries. - Cheryl Aguilar, EthnoBlog Collective emotions in cyber-communities ? Pic. STOP SHREDDING OUR CONSTITUTION, USA 2012 SOCIAL MEDIA & US 2012 ELECTIONS
    9. 9. Collective Emotions in Cyberspace European Union Research Project (FP7 FET) Participant organisation name Leaders Country Specialization Warsaw University of Technology Janusz Hołyst Poland Physics of complex systems EPF Lausanne Ronan Boulic Switzerland Virtual reality University of Wolverhampton Michael Thelwall United Kingdom Webometrics Österreichische 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 networks Jacobs University, Bremen Arvid Kappas Germany Psychophysiology Technical University Berlin Matthias Trier Germany Dynamic network analysis Gemius SA Anna Winnicka 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€
    10. 10. Expected impact of CYBEREMOTIONS • new classes of realistic models of emotionally reacting E-users • new kind of intelligent self-adapting programs, cyber-tutors, cyber-advisors for e- communities (long time scale) • to create theoretical background for the development of the next generation emotionally- intelligent ICT services using universal methods of complex systems (long time scale) . CYBEREMOTIONS = data gathering + complex systems methods + ICT outputs Main aims of Cyberemotions Project were to understand the process of collective emotions formation in e-communities
    11. 11. http://sentistrength.wlv.ac.uk/Univ. Wolverhampton
    12. 12. WP3 created sentiment analysis softwareWP3 created sentiment analysis software - Used for research and for light displays- Used for research and for light displays on the London Eye during the Olympicson the London Eye during the Olympics SentiStrengthSentiStrength
    13. 13. EMG (smiling, frowning) EKG (heart rate) EDA (sweating) Continuous recording of psychophysiology during participation in a forum discussion
    14. 14. Data collected by Wolverhampton group BBC Forum BBC “Religion and Ethics” and “World / UK News” message boards starting from the launch of the website (July 2005 and June 2005 respectively) until the beginning of the crawl (June 2009). #comments 2,474,781 #users 18,045 # threads97,946 Digg The analysis spans the months February to April 2009 and consists of all the stories, comments and users that contributed to the site during this period. The resulting dataset contains approximately 1.9 million stories, 1.6 million comments and 800 thousand users. Blog06 crawl of approximately 100,000 blogs and which spans 11 weeks, from 06/12/2005 to 21/02/2006", i.e. the dataset contains webpages from 100,000 different blogs (more than 3 million webpages) . The blogs are from all over the world, although there is an emphasis on English content #comments 242,057 #discussions 1219 4 million comments Detection of collective emotions in cyber-communities
    15. 15. Emotions (emotional valence e ={ +1,0,-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). Emotional clusters Detection of collective emotions in cyber-communities
    16. 16. Emotional homophily of e-communities α 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.
    17. 17. Collective emotions of cybercommunities detected by various methods t Sentiment Triad Census Analysis Emotional persitence of IRC chatts Emotional clustersEmotional avalanches Hurst eponents
    18. 18. 19 p(e|e) Characteristic exponents α decay linearly with conditional probability of emergence of clusters of size two 2. Collective emotions in cyber-communities )|(8.06.0 eep−=α Minority emotion (less frequent) posses larger value of α - the growth probability is more dependent on cluster size Week interaction Strong interaction Rare emotions create stronger ties
    19. 19. 20 Negative emotions as a fuel for discussion in cyber communities Negative emotionBetter not to be here … A negative emotion results with escape response in real world What about the Internet ?
    20. 20. 21 |<e>| absolute value of the average emotion valence of the first 10 comments <x> Lenght of a thread 20 40 60 80 Number of comments in a thread <e> Average length of a thread as a function of the absolute value of the average emotion valence of the first 10 comments Emotional beginnings of the threads, whether positive or negative, usually lead to longer discussions Negative emotion as a fuel for discussions
    21. 21. WP6/JSI:Emotional Bots can induce collective mood [Ref3]: B. Tadic and M. Suvakov , Arxiv:1305.2741 (2013) joyBot polarizes network of Agents (red links indicate positive emotion messages), while miseryBot induces excess of negative emotion messages (carried by black links ) [Ref3] Simulations revealed how Agents collective emotion polarizes under the influence of positive/negative emotion Bots [Fig.] Bot's impact on Agents can be measured; It relies on the network structure (which propagates emotion among Agents) and on the self-organized nature of the dynamics (which enhances correlations)
    22. 22. WP6/JSI: Agent-Based Model of Chats with Emotional Bots Ref.: V. Gligorijevic, M. Suvakov and B. Tadic, DRAFT (2013) Experimental data: Users group according to their similarity in emotional communications with Bot (5 communities, left); More cohesive groups appear when they are placed in an interactive environment (simulated, right) [Ref.] Agent-Based Model with emotional Agents + Moderators + Bots developed & validated Agents designed with certain 'human' attributes (inferred from the empirical data ) Experimental emotional Bot used as input: response of Agents simulated
    23. 23. AustrianResearchInstituteforArtificialIntelligence–OFAI OFAI , Wien, Interactive Affective Bots Environment [user] [web] Perception Natural Language Understanding Affective Cues Sentiment class ANEW: valence, arousal, dominance LIWC: affective, ling. cognitive categories Action categories, user_ID, channel_ID timestamps Control Interaction Manager Dialog Scripting AIA Report. Module Simulations Collective Users Modelling Individual User Modelling Actuator-Communication Layer WWWWWW • Tools for: • studying affective human- computer interactions: - single user, -multiple users • acquisition of data on users' sentiment towards entities, events, processes • experimental evaluation of theoretical models • • Example realizations of IAB: • Affect Listener Dialog Participant • Affective Interaction Analyser • Affective Supporter and Content Contributor
    24. 24. OFAI, Vienna,Affect Listener - Development of Affective Dialog Systems • Evaluation of systems in 5 experimental setups • Dialog system vs. Wizard of OZ • dialog realism, chatting enjoyment, emotional connection • Effect of system’s affective profile • positive, negative, neutral • Effect of interaction context and roles assigned to the user and system • Effect of fine grained communication scenarios • social sharing of emotions, getting acquainted with someone • Attention and social interactions context - social exclusion
    25. 25. Jacobs University: Social Exclusion by the Conversational System? Mean subjective evaluation of attention paid by the bartender. *** significant difference at p < .0001 . ***
    26. 26. EPFL: emotions in virtual reality Crowd Visualization Software 1. Two H/W platforms: Desktop and CAVE 2. Two S/W platforms: YaQ and Unity3D 3. Pilot S/W OVS v1.1 accessible online Evaluation and Validation WP2 D2.4 Summary Crowd Visualization with Emotion 1.S/W platform: YaQ 2.Number of virtual humans: 200 3.Display rate: more than 60 fps
    27. 27. Sentiment network visualisation (TU Berlin)
    28. 28. Positive subnetwork
    29. 29. Selected project achievements • Emotional responses can be predicted from observation of sentiment fluctuations in physiological and Twitter data. • Asymmetry is crucial for emotion animations in facial expressions. • Sentistrength program used during Olympic Games to monitor daily moods in UK (display at London Eye). • Developed affective bots are capable to communicate with humans. • Emotions can be crucial for leaving of Open Source Community by their active members (including project leaders). • Chat Bots developed for data driven models of e-communities can propagate negative/positive emotions and polarize channel moods. • Chat Bots can lead to social exclusion of e-community members. • Universal tools developed for automatic analysis and visualisation of emotion propagation in social data.
    30. 30. Conclusions . • We demonstrated that collective emotions do exist in a broad class of e-communities • Collective emotional dynamics is vital for the efficiency and survival of e-communities • The current technology makes possible to create bots that can influence human emotions • Understanding the role of and strategic use of cyberemotions will be crucial for the future society because of technological, economical and political issues.
    31. 31. More results will be presented at next presentations, posters and www.cyberemotions.eu CyberEmotions video lectures: https://www.youtube.com/user/fen sPW/videos

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