Entropy-growth-based model of emotionally charged online dialogues

TL;DR

This paper introduces an entropy-growth model for emotionally charged online dialogues, linking emotional entropy to discussion length and proposing a method to artificially extend conversations.

Contribution

It presents a novel entropy-based model for online dialogues and demonstrates its effectiveness in replicating real discussion patterns and controlling discussion duration.

Findings

Entropy growth correlates with discussion length.

Numerical simulations match real data well.

Proposed method can prolong discussions artificially.

Abstract

We analyze emotionally annotated massive data from IRC (Internet Relay Chat) and model the dialogues between its participants by assuming that the driving force for the discussion is the entropy growth of emotional probability distribution. This process is claimed to be correlated to the emergence of the power-law distribution of the discussion lengths observed in the dialogues. We perform numerical simulations based on the noticed phenomenon obtaining a good agreement with the real data. Finally, we propose a method to artificially prolong the duration of the discussion that relies on the entropy of emotional probability distribution.