The wisdom_of_crowds: an efficient, philosophically-validated, social epistemological network profiling toolkit

TL;DR

This paper presents an open-source Python toolkit for efficiently profiling social networks' epistemic diversity and independence, enabling large-scale analysis of online echo chambers and interdisciplinary diversity.

Contribution

It re-implements Sullivan et al.'s method in Python, optimizing it for large networks and demonstrating its practical utility on real-world social media and academic networks.

Findings

Effective large-scale network profiling demonstrated

Identification of echo chambers in social media

Analysis of interdisciplinary diversity in academic networks

Abstract

The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [2020] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence; and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter data [Sullivan et al. 2020]. This paper reports on an open-source re-implementation of their algorithm in Python, optimized to be usable on much larger networks. In addition to the algorithm and package, we also show the ability to scale up our package to large synthetic social network graph profiling, and finally demonstrate its utility in analyzing real-world empirical evidence of `echo chambers' on online social media, as well as evidence of interdisciplinary diversity in an academic communications network.