论文标题

与时代一起移动:使用时间相互作用图研究Alt-Right网络GAB

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

论文作者

Arnold, Naomi A., Steer, Benjamin A., Hafnaoui, Imane, G., Hugo A. Parada, Mondragon, Raul J., Cuadrado, Felix, Clegg, Richard G.

论文摘要

GAB是一个在线社交网络,通常与Alt-Right政治运动以及被禁止其他网络的用户有关。它为研究提供了一个有趣的机会,因为从网络创建的第一天就可以使用近乎完整的数据。在本文中,我们研究了用户交互图的演变,即链接代表在给定时间与其他用户交互的链接。我们在不同的时间和不同的时间尺度上查看此图。后者是通过在图表上使用滑动窗口来实现的,该窗口可为社交网络数据提供新的视角。 GAB网络在几个月内的增长相对较慢,但在数小时和几天内会遭受大量到达。我们确定与最明显的爆发有关的GAB社区感兴趣的合理事件。该网络的特征是“陌生人”之间的互动,而不是加强“朋友”之间的联系。 GAB用法遵循主要基于我们和欧洲的用户的昼夜周期。在非高峰时段,GAB相互作用网络片段进入子网络,它们之间绝对没有相互作用。一小部分用户在较大的时间范围内具有很大的影响力,但是大量用户在短时间内获得了影响。不同时间尺度上的时间分析给出了超出静态图上可以找到的新见解。

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.

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