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Collective Emotions in Cyberspace

www.cyberemotions.eu. 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. Plan.

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Collective Emotions in Cyberspace

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  1. www.cyberemotions.eu 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

  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. There is no agreement what emotions are but they are important for our life !

  4. And they can be useful forfast reactions !!! Negative stimuli negative emotion forces ACTION

  5. Collective effects do matter .... also for emotions !

  6. Collective emotions

  7. 5.09.2012 Satellite meeting at ECCS'12 Emotion in cyberspace ?

  8. Collective emotions in cyber-communities ? SOCIAL MEDIA & US 2012 ELECTIONS TwitterRevolution Pic. Arab Spring in Egypt 2011 congratulations Egypt thecriminalhasleftthepalace – a tweet from Egyptianprotestleader Wael Ghonim. Pic. STOP SHREDDING OUR CONSTITUTION, USA 2012 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 8

  9. Collective Emotions in Cyberspace European Union Research Project (FP7 FET) Large-scale integrating project, ICT Call 3Science of Complex Systems for Socially Intelligent ICT. Duration: 1 Feb. 2009 - 31. Jan. 2013. EC funding 3.6 M€

  10. Main aims of Cyberemotions Project were to understand the process of collective emotions formation ine-communities CYBEREMOTIONS = data gathering + complex systems methods + ICT outputs 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) .

  11. Univ. Wolverhampton http://sentistrength.wlv.ac.uk/

  12. SentiStrength WP3 created sentiment analysis software - Used for research and for light displays on the London Eye during the Olympics

  13. Jacobs University Bremen (WP7) • Continuous recording of psychophysiology during participation in a forum discussion EMG (smiling, frowning) EKG (heart rate) EDA (sweating)

  14. 4 million comments Data collected by Wolverhampton group BBC ForumBBC “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). #comments2,474,781 #users18,045 # threads97,946 Digg The analysis spans the months February to April 2009 and consistsof all the stories, comments and users that contributed to the site duringthis period. The resulting dataset contains approximately 1.9 millionstories, 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 #comments242,057 #discussions1219 Detection of collective emotions in cyber-communities

  15. Emotional clusters 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). Detection of collective emotions in cyber-communities

  16. Emotional homophily of e-communities 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. Collective emotions of cybercommunities detected by various methods Emotional avalanches Emotional clusters t Emotional persitence of IRC chatts Sentiment Triad Census Analysis Hurst eponents

  18. 2. Collective emotions in cyber-communities Characteristic exponents αdecay linearly with conditional probability of emergence of clusters of size two Strong interaction p(e|e) Rare emotions create stronger ties Week interaction Minority emotion (less frequent) posses larger value of α - the growth probability is more dependent on cluster size 19

  19. Negative emotions as a fuel for discussion in cyber communities A negative emotion results with escape response in real world Negative emotion Better not to be here … What about the Internet ? 20

  20. Negative emotion as a fuel for discussions Average length of a thread as a function of the absolute value of the average emotion valence of the first 10 comments Lenght of a thread 20 40 60 80 <x> <e> |<e>| absolute value of the average emotion valence of the first 10 comments Number of comments in a thread Emotional beginnings of the threads, whether positive or negative, usually lead to longer discussions 21

  21. WP6/JSI:Emotional Bots can induce collective mood Simulations revealed how Agents collective emotion polarizes under the influence of positive/negative emotion Bots [Fig.] Bot's impact on Agents can be measured; Itrelies on the network structure (which propagates emotion among Agents) and on the self-organized nature of the dynamics (which enhances correlations) joyBot polarizes network of Agents (red links indicate positive emotion messages), while miseryBot induces excess of negative emotion messages (carried by black links ) [Ref3] [Ref3]: B. Tadic and M. Suvakov , Arxiv:1305.2741 (2013)

  22. WP6/JSI: Agent-Based Model of Chats with Emotional Bots 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 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.] Ref.: V. Gligorijevic, M. Suvakov and B. Tadic, DRAFT (2013)

  23. Austrian Research Institute for Artificial Intelligence – OFAI Environment[user] [web] WWW Actuator-Communication Layer Control Interaction Manager Dialog Scripting AIA Report. Module Simulations Collective Users Modelling Individual User Modelling Perception Natural LanguageUnderstanding Affective Cues Sentiment class ANEW: valence, arousal, dominance LIWC: affective, ling.cognitive categories Action categories, user_ID, channel_IDtimestamps OFAI , Wien, Interactive Affective Bots • 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. 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. Jacobs University: Social Exclusion by the Conversational System? *** Mean subjective evaluation of attention paid by the bartender. *** significant difference at p < .0001 .

  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 WP2 D2.4 Summary Evaluation andValidation • Crowd Visualization with Emotion • S/W platform: YaQ • Number of virtual humans: 200 • Display rate: more than 60 fps

  27. Sentiment network visualisation (TU Berlin)

  28. Positive subnetwork

  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. 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. More results will be presented at next presentations, posters and www.cyberemotions.eu CyberEmotions video lectures: https://www.youtube.com/user/fensPW/videos

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