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Our quantitative insights into user's activity patters and their emotional expressions are eventually combined to model interacting emotional agents.We demonstrate that the stylised facts of the emotional persistence can be reproduced by our model by only calibrating a small set of agent features.Validating our agent-based model against empirical findings allows us to draw conclusions about the role of emotions in this form of communication.Online communication can be seen as a large-scale social experiment that constantly provides us with data about user activities and interactions.We process our analysis as follows: first, we look into the communication patterns of instant online discussions, to find out about the average response time of users and its possible dependence on the topics discussed.This shall allow us to identify differences between instantaneous chatting communities and other forms of slower, persistent communication.This type of interaction requires much higher user activity in comparison to persistent communication e.g. Further, it is more spontaneous, often leading to emotionally-rich communication between involved peers.
B) Probability distribution of the user activity over all the IRC channels.Analysing the emotional expressions (positive, negative, neutral) of users, we revealed a remarkable persistence both for individual users and channels. 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.This success indicates that our modeling framework can be used to test further hypothesis about emotional interaction in online communities.A) Schema of the evolution of a conversation in an IRC channel.