Adaptive Causal Coordination Detection for Social Media: A Memory-Guided Framework with Semi-Supervised Learning

TL;DR

The paper introduces ACCD, a memory-guided, semi-supervised framework that dynamically detects coordinated inauthentic behavior on social media with high accuracy and reduced manual effort.

Contribution

It presents a novel three-stage adaptive framework combining causal analysis, active semi-supervised learning, and self-verification for social media coordination detection.

Findings

Achieves 87.3% F1-score in detection tasks.

Reduces manual labeling by 68%.

Speeds up processing by 2.8 times.

Abstract

Detecting coordinated inauthentic behavior on social media remains a critical and persistent challenge, as most existing approaches rely on superficial correlation analysis, employ static parameter settings, and demand extensive and labor-intensive manual annotation. To address these limitations systematically, we propose the Adaptive Causal Coordination Detection (ACCD) framework. ACCD adopts a three-stage, progressive architecture that leverages a memory-guided adaptive mechanism to dynamically learn and retain optimal detection configurations for diverse coordination scenarios. Specifically, in the first stage, ACCD introduces an adaptive Convergent Cross Mapping (CCM) technique to deeply identify genuine causal relationships between accounts. The second stage integrates active learning with uncertainty sampling within a semi-supervised classification scheme, significantly reducing the burden of manual labeling. The third stage deploys an automated validation module driven by historical detection experience, enabling self-verification and optimization of the detection outcomes. We conduct a comprehensive evaluation using real-world datasets, including the Twitter IRA dataset, Reddit coordination traces, and several widely-adopted bot detection benchmarks. Experimental results demonstrate that ACCD achieves an F1-score of 87.3\% in coordinated attack detection, representing a 15.2\% improvement over the strongest existing baseline. Furthermore, the system reduces manual annotation requirements by 68\% and achieves a 2.8x speedup in processing through hierarchical clustering optimization. In summary, ACCD provides a more accurate, efficient, and highly automated end-to-end solution for identifying coordinated behavior on social platforms, offering substantial practical value and promising potential for broad application.