  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.
