Narrative Interaction in Online Networks

Agent-Based Modeling Belief Modeling Narratives Network Science Simulation

// Quick Summary

How are narrative interactions shaped by network structure and individual beliefs about events?
Combines behavioral experiments in social networks, LLM-powered agent-based modeling, and neural embedding-based language modeling
Developed software for running network experiments on narrative interaction and modeling narrative interaction behaviors
Includes published articles, open-source technology, and LLM-based simulation tools

Quick Access — Open Data and Code

Network Experiment GitHub Python Notebook — Narrative Alignment

Project Overview

While narratives have shaped human beliefs and cultures for centuries, the internet has introduced new narrative phenomena. Long-standing cognitive psychology paradigms and computer science methodologies are poorly equipped to study digital forms of narrative interaction and networked communication.

This project investigates how social network structure, narrative content, and communication contexts influence the formation and convergence of shared narratives in digital environments.

Large-scale behavioral experiments in custom-built online social networks
AI-based network simulations with LLM-powered generative agents (LLaMA, Qwen, DeepSeek, Gemma)
Novel natural language modeling approaches to quantify individual and group-level narrative alignment and belief dynamics

Key Findings

Spatially-embedded network structures produce echo chambers while fully-connected structures produce shared behaviors

Echo chamber onset in spatially-embedded vs fully-connected networks — Rewards and colormaps of hashtag responses across a single N = 20 run. Top panel shows results for a spatially-embedded network, the bottom panel is from a fully-connected network. Left: Network structure with player nodes sized by participants' final rewards for coordinating. Right: Colormap of individual responses, rows represent individual participants' set of responses, columns represent trials.

Information complexity of narratives moderates the effect of network structure on group consensus

Belief dynamics in network experiments — Onset of behavioral coherence during networked interaction. Panels display the proportion of each group adopting a dominant response over course of interaction by group size (columns) and interaction media content (rows). Each line represents a single experimental run from a group of participants.

Narrative complexity moderates group outcomes by encouraging different social learning strategies

Social learning strategies in network experiments — Dynamics of individual decision strategy during networked interaction. Each panel illustrates the temporal dynamics of the proportion of each group adopting one of four decision strategies (sampling new responses, repeating a partner's last response, repeating one's own previous response, and resampling from earlier context) across 40 trials in different network structures and interaction contents.

Why It Matters

Shared and polarized narratives don't emerge randomly — they evolve through networked interaction. Narrative interactions are shaped by individuals' causal background knowledge, the social rewards of aligning beliefs and behaviors with others, and the network topology (who can talk to whom) of communication. This project explains:

Why some groups converge on shared interpretations of information while others fragment
How digital environments including prompting instructions can facilitate group consensus rather than disagreement
Business use case: Social platforms or media organizations can apply these findings to optimize content prompts or feed structures that encourage consensus, diversity, or specific forms of engagement.
Research use case: Social scientists can use this paradigm to experimentally manipulate network properties and observe real-time narrative change, and align human group behavior with simulation data from AI agents.

How It Works

Overview of network experiment design — Experiment procedure and networked interaction tasks. The experimental design follows three blocks. In the pre-interaction block, all participants read the Fukushima nuclear disaster narrative. Participants then wrote a tweet-like personal narrative about the disaster and generated ten hashtags describing the event. Participants next entered a network interaction block where they communicated with network neighbors for 40 trials, receiving points for coordination.

Experimental Network Design: Participants interact in custom-built online social networks with two topologies:
- Homogeneous: each node connected to all others
- Spatial: ring-lattice with four neighbors
Narrative Stimuli: Participants engage with narratives designed to elicit causal interpretations
Hashtag Rounds (x40): Incentivized coordination tasks over 40 trials per network
Natural Language Analysis: LLM-based semantic similarity of hashtags and personal narratives used to track group-level belief alignment
Multilevel Bayesian Modeling: Predicts hashtag convergence, group entropy, and narrative content as a function of network conditions
Simulated Generative Agents: LLM-based networks simulate trends to replicate experimental outcomes

Related Publications

Priniski, J.H., et al. (2024). Online network topology shapes personal narratives and hashtag generation. Proceedings of the Cognitive Science Society. eScholarship
Priniski, J.H., et al. (2025). Effect-prompting shifts narrative framing of networked interactions. Proceedings of the Cognitive Science Society. PDF
Priniski, J.H., et al. (Working Paper). Neighborhood topology shapes narrative interaction dynamics in networked groups. PDF
Jha, A., Priniski, J., & Morstatter, F. (Submitted). Simulating narrative interaction dynamics with generative agents.
Priniski, J.H. (Working Paper). Reinforcement learning model of narrative interaction.