Collective emotion dynamics in chats with agents, moderators and Bots

TL;DR

This study uses agent-based simulations to explore how collective emotional states emerge and evolve in chat networks with moderators and emotional Bots, revealing self-organizing effects and fractal dynamics.

Contribution

It introduces a realistic chat system model with empirically inferred parameters, analyzing how Bots influence collective emotions and the underlying complex dynamics.

Findings

Positive Bot emotions lead to more explosive collective responses.

Emotion matching Bot polarity results in higher event clustering.

Relaxation dynamics are driven by external noise, independent of Bot activity.

Abstract

Using agent-directed simulations, we investigate fluctuations in the collective emotional states on a chat network where agents interchange messages with a fixed number of moderators and emotional Bot. To design a realistic chat system, the interaction rules and some statistical parameters, as well as the agent's attributes, are inferred from the empirical chat channel \texttt{Ubuntu}. In the simulations, the Bot's emotion is fixed; the moderators tune the level of its activity by passing a fraction $\epsilon$ of messages to the Bot. At $\epsilon \gtrsim 0$, the collective emotional state matching the Bot's emotion polarity gradually arises; the average growth rate of the dominant emotional charge serves as an order parameter. Due to self-organizing effects, the collective dynamics is more explosive when positive emotions arise by positive Bot than the onset of negative emotions in the presence of negative Bot at the same $\epsilon$. Furthermore, when the emotions matching the Bot's emotion polarity are spread over the system, the underlying fractal processes exhibit higher persistence and stronger clustering of events than the processes spreading of emotion polarity opposite to the Bot's emotion. On the other hand, the relaxation dynamics is controlled by the external noise; the related nonextensive parameter, estimated from the statistics of returns, is virtually independent of the Bot's activity level and emotion contents.