Coordinated online behaviors are an essential part of information and
influence operations, as they allow a more effective disinformation's spread.
Most studies on coordinated behaviors involved manual investigations, and the
few existing computational approaches make bold assumptions or oversimplify the
problem to make it tractable. Here, we propose a new network-based framework
for uncovering and studying coordinated behaviors on social media. Our research
extends existing systems and goes beyond limiting binary classifications of
coordinated and uncoordinated behaviors. It allows to expose different
coordination patterns and to estimate the degree of coordination that
characterizes diverse communities. We apply our framework to a dataset
collected during the 2019 UK General Election, detecting and characterizing
coordinated communities that participated in the electoral debate. Our work
conveys both theoretical and practical implications and provides more nuanced
and fine-grained results for studying online information manipulation.

我们提出了一种基于网络的框架，用于揭示和研究社交媒体上协调行为，扩展现有系统并允许暴露不同的协调模式以及估计表征不同社区的协调程度。我们将我们的框架应用于收集在 2019 年英国大选期间的数据集，检测并表征了参与选举辩论的协调社区。我们的工作提供了研究在线信息操纵的更加细粒度和微妙的结果，具有重要的理论和实践意义。

2019 英国大选社交媒体上的协调行为

Coordinated Behavior on Social Media in 2019 UK General Election

Efficient and reliable social bot classification is crucial for detecting
information manipulation on social media. Despite rapid development,
state-of-the-art bot detection models still face generalization and scalability
challenges, which greatly limit their applications. In this paper we propose a
framework that uses minimal account metadata, enabling efficient analysis that
scales up to handle the full stream of public tweets of Twitter in real time.
To ensure model accuracy, we build a rich collection of labeled datasets for
training and validation. We deploy a strict validation system so that model
performance on unseen datasets is also optimized, in addition to traditional
cross-validation. We find that strategically selecting a subset of training
data yields better model accuracy and generalization than exhaustively training
on all available data. Thanks to the simplicity of the proposed model, its
logic can be interpreted to provide insights into social bot characteristics.

本研究提出一种基于最少账户元数据的框架，实现了对 Twitter 公共推文全量实时分析的高效和可靠的社交机器人分类，通过对训练和验证的大量数据集进行严格的验证系统，找到了挑选子集进行训练的方式比全量训练更好的模型精度和泛化性。