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.
This paper uses the Raphtory temporal graph software to investigate the alt-right social network Gab. We show that when looking even at simple questions like "is the network growing" the choice of timescale is important. We show that the network grows rapidly in usership at a small timescale (days) in response to events of interest to the user group. However, we also show that over a longer timescale (months) the growth is very slow or non-existant. We also observe for the first time the authors know of, a social network where the weakly connected component shatters in off-peak hours. At peak the graph has (as would be expected) most users connected in a single WCC but in off-peak hours this is less than half the userbase.