Inferring information processing and emotion spreading in online communities

Our society becomes more and more a coevolving techno-social system where the accelerating progress of information technologies induces quantitative changes in the way we communicate, express our opinions and emotions.

The lecture will be started with a demonstration of a simple model of coevolution of information processing and topology in hierarchical adaptive random boolean networks. Then I discuss three more realistic cases related to: (i) prediction of Twitter coevolution, (ii) locating a hidden source of information spreading in complex networks and (iii) tracking of information flow in Slovenian Press Agency STA .

The second part of my lecture will be devoted to detecting and modelling of collective emotions in cyberspace. I will address the following questions: 
what are emotions ? How can one measure emotional states? What are cyberemotions ? 
When do emotions and cyberemotions become collective phenomena? 
 
What role do emotions play for on-line communities? I will show how machine learning methods can become an efficient tool for large scale sentiment analysis and how data-driven agent-based models of virtual emotional human can describe live and death of on-line communities.

References:
1. “Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks”, Piotr J. Górski, Agnieszka Czaplicka and Janusz A. Hołyst, Eur. Phys. J. B (2016) 89: 33. doi:10.1140/epjb/e2015-60530-6
2. “Cyberemotions – Collective Emotions in Cyberspace”, Janusz A. Hołyst (Ed.), Springer 2017.

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