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

Weng Ding, Yi Han, Mu-Jiang-Shan Wang

TL;DR

This work tackles coordinated inauthentic behavior on social media by moving beyond static, correlation-based detection to a memory-guided, adaptive three-stage framework called ACCD. The approach integrates adaptive Convergent Cross Mapping for robust causal inference, a semi-supervised user classifier with active learning to reduce labeling effort, and an experience-driven automated validator for dataset-aware model selection. Empirical results on the Twitter IRA dataset, Reddit coordination traces, and TwiBot-20 show substantial improvements, including an F1 gain of 15.2 percentage points over baselines, about a 68% reduction in manual labeling, and a 2.8x processing speedup thanks to hierarchical clustering and memory-guided configurations. Together, ACCD offers an accurate, scalable, and largely automated solution for detecting coordinated behavior on social platforms with strong practical impact and broad applicability.

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.

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

TL;DR

This work tackles coordinated inauthentic behavior on social media by moving beyond static, correlation-based detection to a memory-guided, adaptive three-stage framework called ACCD. The approach integrates adaptive Convergent Cross Mapping for robust causal inference, a semi-supervised user classifier with active learning to reduce labeling effort, and an experience-driven automated validator for dataset-aware model selection. Empirical results on the Twitter IRA dataset, Reddit coordination traces, and TwiBot-20 show substantial improvements, including an F1 gain of 15.2 percentage points over baselines, about a 68% reduction in manual labeling, and a 2.8x processing speedup thanks to hierarchical clustering and memory-guided configurations. Together, ACCD offers an accurate, scalable, and largely automated solution for detecting coordinated behavior on social platforms with strong practical impact and broad applicability.

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.
Paper Structure (16 sections, 12 equations, 16 figures, 2 tables, 1 algorithm)

This paper contains 16 sections, 12 equations, 16 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Causal Relationship-Based Detection
  4. Behavioral Pattern Classification
  5. Automated Causal Inference Frameworks
  6. Preliminaries
  7. Method
  8. Adaptive Causal Coordination Detector
  9. Semi-Supervised User Classifier
  10. Adaptive Causal Validator
  11. Experiments
  12. Experimental Settings
  13. Main Results
  14. Case Study
  15. Ablation Study
  16. ...and 1 more sections

Figures (16)

  • Figure 1: Motivation of ACCD. From static, correlation-based detection to an adaptive, memory-guided three-stage framework.
  • Figure 2: Architecture of the proposed ACCD framework with adaptive causal detection, semi-supervised classification, and causal validation.
  • Figure 4: Computational efficiency analysis showing memory usage and speed improvements across different user set sizes.
  • Figure 6: Ablation study showing the contribution of each ACCD component.
  • Figure : (a) F1-score comparison on Twitter IRA data set.
  • ...and 11 more figures