Challenges of Growing Social Media Networks From the Bottom-Up Through the Agent Perspective

TL;DR

This paper develops an agent-based model to understand how simple rules can generate social media networks, highlighting challenges and proposing solutions for more accurate future modeling.

Contribution

The paper introduces a novel agent-based modeling approach to replicate Twitter network structures and discusses the difficulties encountered in accurately reproducing real-world social media networks.

Findings

Successful reproduction of some network properties

Identification of key challenges in modeling social media networks

Proposals for improving agent-based models in future work

Abstract

We develop an agent-based model in order to understand agent/node behaviors that generate social media networks. We use simple rules to synthetically generate a backcloth (friend/follow) network collected using Twitter's API. The Twitter network was collected using seeds for known terrorist propaganda accounts in 2015. Model parameter adjustments were made to reproduce the collected network's summary statistics, stylized facts and general structural measures. We produced an approximate network in line with the general properties of our collected data. We present our findings with a focus on the challenging aspects of this reproduction. We find that while it is possible to generate a social media network utilizing a few simple rules, numerous challenges arise requiring departure from the agent viewpoint and the development of more useful methods. We present numerous weaknesses and challenges in our reproduction and propose potential solutions for future efforts.