  Recent years have witnessed an increasing use of coordinated accounts on
social media, operated by misinformation campaigns to influence public opinion
and manipulate social outcomes. Consequently, there is an urgent need to
develop an effective methodology for coordinated group detection to combat the
misinformation on social media. However, existing works suffer from various
drawbacks, such as, either limited performance due to extreme reliance on
predefined signatures of coordination, or instead an inability to address the
natural sparsity of account activities on social media with useful prior domain
knowledge. Therefore, in this paper, we propose a coordination detection
framework incorporating neural temporal point process with prior knowledge such
as temporal logic or pre-defined filtering functions. Specifically, when
modeling the observed data from social media with neural temporal point
process, we jointly learn a Gibbs-like distribution of group assignment based
on how consistent an assignment is to (1) the account embedding space and (2)
the prior knowledge. To address the challenge that the distribution is hard to
be efficiently computed and sampled from, we design a theoretically guaranteed
variational inference approach to learn a mean-field approximation for it.
Experimental results on a real-world dataset show the effectiveness of our
proposed method compared to the SOTA model in both unsupervised and
semi-supervised settings. We further apply our model on a COVID-19 Vaccine
Tweets dataset. The detection result suggests the presence of suspicious
coordinated efforts on spreading misinformation about COVID-19 vaccines.
