Quoting is not Citing: Disentangling Affiliation and Interaction on Twitter

TL;DR

This paper investigates the differences between interaction and affiliation networks on Twitter, revealing complex cross-cutting patterns that challenge the simple echo chamber narrative through statistical analysis of political valence and network behaviors.

Contribution

It introduces a method to distinguish between interaction and affiliation networks on Twitter and analyzes their differing patterns of political homophily and cross-cutting links.

Findings

Interaction networks are less homophilic than affiliation networks.

Cross-cutting links are prevalent in interaction networks, challenging the echo chamber concept.

Political valence can be statistically assigned based on network affiliation patterns.

Abstract

Interaction networks are generally much less homophilic than affiliation networks, accommodating for many more cross-cutting links. By statistically assigning a political valence to users from their network-level affiliation patterns, and by further contrasting interaction and affiliation (quotes and retweets) within specific discursive events, namely quote trees, we describe a variety of cross-cutting patterns which significantly nuance the traditional "echo chamber" narrative.