Political misinformation, astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Many previous approaches examining these phenomena have focused on broad campaigns rather than the small groups responsible for instigating or sustaining them. To reveal latent (i.e., hidden) networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in various behaviours that, in concert, come to execute different goal-based strategies, a number of which we describe. The approach relies upon a pipeline that extracts relevant elements from social media posts, infers connections between accounts based on criteria matching the coordination strategies to build an undirected weighted network of accounts, which is then mined for communities exhibiting high levels of evidence of coordination using a novel community extraction method. We address the temporal aspect of the data by using a windowing mechanism, which may be suitable for near real-time application. We further highlight consistent coordination with a sliding frame across multiple windows and application of a decay factor. Our approach is compared with other recent similar processing approaches and community detection methods and is validated against two relevant datasets with ground truth data, using content, temporal, and network analyses, as well as with the design, training and application of three one-class classifiers built using the ground truth; its utility is furthermore demonstrated in two case studies of contentious online discussions.
翻译:以往许多审查这些现象的方法都侧重于广泛的运动,而不是负责煽动或维持这些现象的小型团体。为了揭示潜在的(即隐藏的)合作账户网络,我们提议采用新的时间窗口方法,仅依靠账户互动和元数据。我们建议采用新的时间窗口方法,仅依靠账户互动和元数据。我们发现参与各种行为的账户群,这些账户群可能合力执行不同的基于目标的战略,我们所描述的一些行为。该方法依赖于从社交媒体站提取相关要素的管道,根据与协调战略相匹配的标准推断账户间的联系,以建立一个不受指导的加权账户网络,然后利用新的社区提取方法为显示高度协调证据的社区进行挖掘。我们利用窗口机制处理数据的时间方面问题,可能适合近实时应用。我们进一步强调与跨多个窗口的滑动框架和衰败因素的应用相一致。我们的方法与其他最近的类似处理方法和社区检测方法进行比较,并根据两个相关的数据组进行验证,以地面数据集为基础,使用新的社区提取法方法,利用实时、网络和在线分析,进一步进行实地数据分析,同时进行实地数据分析,并进行实地分析。