Can human-like Bots control collective mood: agent-based simulations of online chats

Using an agent-based modeling approach, in this paper, we study self-organized dynamics of interacting agents in the presence of chat Bots. Different Bots with tunable ‘human-like’ attributes, which exchange emotional messages with agents, are considered, and the collective emotional behavior of agents is quantitatively analyzed. In particular, using detrended fractal analysis we determine persistent fluctuations and temporal correlations in time series of agent activity and statistics of avalanches carrying emotional messages of agents when Bots favoring positive/negative affects are active. We determine the impact of Bots and identify parameters that can modulate that impact. Our analysis suggests that, by these measures, the emotional Bots induce collective emotion among interacting agents by suitably altering the fractal characteristics of the underlying stochastic process. Positive emotion Bots are slightly more effective than negative emotion Bots. Moreover, Bots which periodically alternate between positive and negative emotion can enhance fluctuations in the system, leading to avalanches of agent messages that are reminiscent of self-organized critical states.