# Network structure shapes consensus dynamics through individual decisions

## TL;DR

This study explores how network structures in online social networks influence how shared narratives and beliefs emerge through individual decisions and interactions.

## Contribution

The paper introduces a new experimental and modeling approach to study how network structure affects narrative alignment and semantic content in decentralized online networks.

## Key findings

- Fully connected networks focus on event causes, while locally connected networks emphasize event effects.
- Participants in fully connected networks showed increased causal language in personal narratives after interaction.
- Network structure influences representational and language changes in shared narratives.

## Abstract

Digital media and online social networks have upended how narratives are constructed and shared, shaping cognition and culture in unexpected ways. Individuals within these networks have increased narrative agency, which enables them to directly contribute to and share evolving stories. Understanding the reflexive processes between individual and networked group narrative dynamics requires new forms of behavioral experimentation and modeling. We conducted a large-scale online social network experiment on narrative interaction, analyzed language dynamics using agent-based modeling, and developed quantitative measures of narrative alignment. Results reveal how network structure interacts with individual decision-making to influence the dynamics and semantic content of shared beliefs, with implications for understanding how narrative information flows through online networks with different neighborhood connections.

How do shared narratives emerge in decentralized online networks? Prior research using simplified group coordination tasks (e.g., face-naming) shows network structure shapes group consensus, but the underlying cognitive mechanisms remain unclear. Here, we examine how network structure influences the emergence and semantic content of shared narrative beliefs in experimental online social networks, using natural language processing measures and agent-based modeling. Media content with complex causal structure attenuates network structure effects by encouraging longer exploration of background knowledge. Yet network structure still shapes the narrative content communicated. An embedding-based narrative alignment measure shows that fully connected groups orient their interactions more toward communicating causes of an event, whereas locally connected networks emphasize the event’s effects. A group’s network structure also influences representational and language change in personal narratives: participants in fully connected networks showed the largest increase in causal language in personal narratives written after interaction, which also orient more around the narrative’s causal events.