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

Speaker: Benjamin Alexander Steer

Date/Time: 18-Feb-2021, 16:00 UTC

Venue: Virtual Seminar


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.

Bio: Ben is a researcher at Queen Mary University of London/The Alan Turing Institute and a cofounder at Chorograph. His research in complex networks and distributed systems focuses on the development of Raphtory, a large-scale stream processing solution for temporal graph analytics. His secondary focus as a board member for the Linked data benchmark council is to define frameworks where different graph-based technologies can be fairly teste/compared and help researchers open new frontiers in high-performance graph data management.

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