Moving with the Times: Investigating the Alt-Right Network Gab with Temporal Interaction Graphs

TL;DR

This study analyzes the evolution of the Gab social network using temporal interaction graphs, revealing insights into user behavior, influence patterns, and community structure over time and across different timescales.

Contribution

It introduces a novel temporal analysis approach with sliding windows to study social network dynamics, providing new perspectives on user interactions and influence.

Findings

Gab's user interactions are mostly between strangers rather than friends.

The network exhibits diurnal cycles with fragmentation during off-peak hours.

Influence is concentrated among a small group of users over long timescales.

Abstract

Gab is an online social network often associated with the alt-right political movement and users barred from other networks. It presents an interesting opportunity for research because near-complete data is available from day one of the network's creation. In this paper, we investigate the evolution of the user interaction graph, that is the graph where a link represents a user interacting with another user at a given time. We view this graph both at different times and at different timescales. The latter is achieved by using sliding windows on the graph which gives a novel perspective on social network data. The Gab network is relatively slowly growing over the period of months but subject to large bursts of arrivals over hours and days. We identify plausible events that are of interest to the Gab community associated with the most obvious such bursts. The network is characterised by interactions between `strangers' rather than by reinforcing links between `friends'. Gab usage follows the diurnal cycle of the predominantly US and Europe based users. At off-peak hours the Gab interaction network fragments into sub-networks with absolutely no interaction between them. A small group of users are highly influential across larger timescales, but a substantial number of users gain influence for short periods of time. Temporal analysis at different timescales gives new insights above and beyond what could be found on static graphs.