VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media

TL;DR

This paper introduces VigDet, a neural temporal point process model incorporating prior knowledge to detect coordinated groups on social media, effectively identifying misinformation campaigns and suspicious activities.

Contribution

It proposes a novel knowledge-informed neural temporal point process framework with a variational inference method for efficient coordination detection on social media.

Findings

Outperforms state-of-the-art models in unsupervised and semi-supervised settings.

Successfully detects suspicious coordination in COVID-19 vaccine misinformation.

Demonstrates effectiveness on real-world social media datasets.

Abstract

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.