# An agent-based model for emotion contagion and competition in online social media

## TL;DR

This paper introduces an agent-based model that simulates emotion contagion and competition in online social media, revealing how emotions like anger and joy spread and dominate communities under various conditions.

## Contribution

It presents a comprehensive, empirically-estimable agent-based model incorporating emotion influence, tie strength, and emotion competition, advancing understanding of online emotion dynamics.

## Key findings

- Anger-dominated users have higher vitality than joy-dominated ones.
- Anger prefers weaker ties for diffusion, facilitating spread across groups.
- A critical gap of less than 10% between anger and joy ratios leads to anger dominance.

## Abstract

Recent studies suggest that human emotions diffuse in not only real-world communities but also online social media. More and more mechanisms beyond emotion contagion are revealed, including emotion correlations which indicate their influence and the coupling of emotion diffusion and network structure such as tie strength. Besides, different emotions might even compete in shaping the public opinion. However, a comprehensive model that considers up-to-date findings to replicate the patterns of emotion contagion in online social media is still missing. In this paper, to bridge this vital gap, we propose an agent-based emotion contagion model which combines features of emotion influence and tie strength preference in the dissemination process. The simulation results indicate that anger-dominated users have higher vitality than joy-dominated ones, and anger prefers weaker ties than joy in diffusion, which could make it easier to spread between online groups. Moreover, anger's high influence makes it competitive and easily to dominate the community, especially when negative public events occur. It is also surprisingly revealed that as the ratio of anger approaches joy with a gap less than 10%, angry tweets and users will eventually dominate the online social media and arrives the collective outrage in the cyber space. The critical gap disclosed here can be indeed warning signals at early stages for outrage controlling in online social media. All the parameters of the presented model can be easily estimated from the empirical observations and their values from historical data could help reproduce the emotion contagion of different circumstances. Our model would shed lights on the study of multiple issues like forecasting of emotion contagion in terms of computer simulations.