Flow of emotional messages in artificial social networks

TL;DR

This paper models how emotional messages propagate in artificial social networks, showing how message dynamics and memory effects influence the evolution of communication links and social sharing behaviors.

Contribution

It introduces a novel simulation framework for emotional message flow, incorporating link weight evolution, memory effects, and secondary sharing phenomena.

Findings

Link weights evolve based on message exchange frequency.

Memory effects influence message receiver selection.

Secondary sharing amplifies emotional message dissemination.

Abstract

Models of message flows in an artificial group of users communicating via the Internet are introduced and investigated using numerical simulations. We assumed that messages possess an emotional character with a positive valence and that the willingness to send the next affective message to a given person increases with the number of messages received from this person. As a result, the weights of links between group members evolve over time. Memory effects are introduced, taking into account that the preferential selection of message receivers depends on the communication intensity during the recent period only. We also model the phenomenon of secondary social sharing when the reception of an emotional e-mail triggers the distribution of several emotional e-mails to other people.