Emotional persistence in online chatting communities

How do users behave in online chatrooms, where they instantaneously read and write posts? We analyzed about 2.5 million posts covering various topics in Internet relay channels, and found that user activity patterns follow known power-law and stretched exponential distributions, indicating that online chat activity is not different from other forms of communication. Analysing the emotional expressions (positive, negative, neutral) of users, we revealed a remarkable persistence both for individual users and channels. I.e. despite their anonymity, users tend to follow social norms in repeated interactions in online chats, which results in a specific emotional “tone” of the channels. We provide an agent-based model of emotional interaction, which recovers qualitatively both the activity patterns in chatrooms and the emotional persistence of users and channels. While our assumptions about agent's emotional expressions are rooted in psychology, the model allows to test different hypothesis regarding their emotional impact in online communication.

[1]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[2]  J. Russell.  A circumplex model of affect. , 1980 .

[3]  M. Holbrook,et al.  Assessing the Role of Emotions as Mediators of Consumer Responses to Advertising , 1987 .

[4]  E. Tronick.  Emotions and emotional communication in infants. , 1989, The American psychologist.

[5]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[6]  B. Rimé,et al.  Exposure to the social sharing of emotion: Emotional impact, listener responses and secondary social sharing , 1997 .

[7]  S. Preston,et al.  Empathy: Its ultimate and proximate bases. , 2001, The Behavioral and brain sciences.

[8]  F. Schweitzer.  Brownian Agents and Active Particles , 2003, Springer Series in Synergetics.

[9]  Frank Schweitzer,et al.  Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences , 2003 .

[10]  H. Kantz,et al.  Recurrence time analysis, long-term correlations, and extreme events. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  A. Barabasi,et al.  Human dynamics: Darwin and Einstein correspondence patterns , 2005, Nature.

[12]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[13]  Shlomo Havlin,et al.  Long-term memory: a natural mechanism for the clustering of extreme events and anomalous residual times in climate records. , 2005, Physical review letters.

[14]  J. G. Oliveira,et al.  Human Dynamics: The Correspondence Patterns of Darwin and Einstein , 2005 .

[15]  Albert-László Barabási,et al.  Modeling bursts and heavy tails in human dynamics , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Kazuko Yamasaki,et al.  Scaling and memory of intraday volatility return intervals in stock markets. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  James W. Pennebaker,et al.  Linguistic Inquiry and Word Count (LIWC2007) , 2007 .

[18]  Adilson E. Motter,et al.  A Poissonian explanation for heavy tails in e-mail communication , 2008, Proceedings of the National Academy of Sciences.

[19]  G. Madey,et al.  Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.

[20]  R Linsker,et al.  Power-law and exponential tails in a stochastic priority-based model queue. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Oleg E. Melnik,et al.  Encyclopedia of Complexity and Systems Science , 2008 .

[22]  Bosiljka Tadic,et al.  Bloggers behavior and emergent communities in Blog space , 2009, ArXiv.

[23]  Jan W. Kantelhardt.  Fractal and Multifractal Time Series , 2009, Encyclopedia of Complexity and Systems Science.

[24]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[25]  S. Havlin,et al.  Scaling laws of human interaction activity , 2009, Proceedings of the National Academy of Sciences.

[26]  B. Rimé.  Emotion Elicits the Social Sharing of Emotion: Theory and Empirical Review , 2009 .

[27]  Didier Sornette,et al.  Power law signature of media exposure in human response waiting time distributions. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Jürgen Kurths,et al.  Evidence for a bimodal distribution in human communication , 2010, Proceedings of the National Academy of Sciences.

[29]  D. Thalmann,et al.  Sentiment analysis of informal textual communication in cyberspace , 2010 .

[30]  C. Izard.  The Many Meanings/Aspects of Emotion: Definitions, Functions, Activation, and Regulation , 2010 .

[31]  Frank Schweitzer,et al.  An agent-based model of collective emotions in online communities , 2010, ArXiv.

[32]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[33]  ThelwallMike,et al.  Sentiment strength detection in short informal text , 2010 .

[34]  F. Tuerlinckx,et al.  Journal of Personality and Social Psychology Feelings Change : Accounting for Individual Differences in the Temporal Dynamics of Affect , 2010 .

[35]  Sergey V. Buldyrev,et al.  Communication activity in social networks: growth and correlations , 2010, 1002.0216.

[36]  Frank Schweitzer,et al.  Emotions in Product Reviews--Empirics and Models , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[37]  Lada A. Adamic.  Complex systems: Unzipping Zipf's law , 2011, Nature.

[38]  Georgios Paltoglou,et al.  Affect Bartender — Affective cues and their application in a conversational agent , 2011, 2011 IEEE Workshop on Affective Computational Intelligence (WACI).

[39]  Mike Thelwall,et al.  Negative emotions boost user activity at BBC forum , 2010, 1011.5459.

[40]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..

[41]  Sebastian Bernhardsson,et al.  Zipf's law unzipped , 2011, ArXiv.

[42]  Frank Schweitzer,et al.  Positive words carry less information than negative words , 2011, EPJ Data Science.

[43]  Berkant Barla Cambazoglu,et al.  A large-scale sentiment analysis for Yahoo! answers , 2012, WSDM '12.

[44]  Kimmo Kaski,et al.  Circadian pattern and burstiness in mobile phone communication , 2011, 1101.0377.

[45]  Mike Thelwall,et al.  Sentiment strength detection for the social web , 2012, J. Assoc. Inf. Sci. Technol..