Entropy-growth-based model of emotionally charged online dialogues
This work addresses the modeling of emotional dynamics in online conversations, which is incremental as it builds on existing entropy-based approaches to analyze specific chat data.
The authors tackled the problem of modeling emotionally charged online dialogues by analyzing IRC data and proposing that entropy growth of emotional probability distribution drives discussions, correlating with observed power-law distribution of discussion lengths, and they achieved good agreement with real data through numerical simulations.
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.