The quoter model: a paradigmatic model of the social flow of written information

TL;DR

This paper introduces the quoter model, a symbolic network model simulating social media information flow through quoting, enabling analysis of information transfer and validation of flow measures using simulations.

Contribution

The paper presents a novel symbolic network model that captures social media information flow via quoting, with analytic relationships and validation through simulations.

Findings

Model accurately simulates information flow in social networks.

Analytic relationships link model parameters to information-theoretic estimators.

Simulations demonstrate the model's utility in testing information flow measures.

Abstract

We propose a model for the social flow of information in the form of text data, which simulates the posting and sharing of short social media posts. Nodes in a graph representing a social network take turns generating words, leading to a symbolic time series associated with each node. Information propagates over the graph via a quoting mechanism, where nodes randomly copy short segments of text from each other. We characterize information flows from these text via information-theoretic estimators, and we derive analytic relationships between model parameters and the values of these estimators. We explore and validate the model with simulations on small network motifs and larger random graphs. Tractable models such as ours that generate symbolic data while controlling the information flow allow us to test and compare measures of information flow applicable to real social media data. In particular, by choosing different network structures, we can develop test scenarios to determine whether or not measures of information flow can distinguish between true and spurious interactions, and how topological network properties relate to information flow.