A hybrid neural-symbolic approach for the longitudinal profiling of coercive control in digital investigations

The exponential growth of text-based digital evidence, particularly in mobile chat logs, challenges current forensic workflows. This is especially acute in investigations of coercive control, where evidence manifests not as a single ‘smoking gun’ but as a cumulative behavioural profiling of psychological abuse often missed by standard keyword searches. To address this data-to-insight gap, we present the Digital Conversation Analysis Pipeline (DCAP), a proof-of-concept Human-in-the-Loop (HITL) framework designed for investigative triage. The pipeline parses heterogeneous text-based evidence and analyses it using a novel hybrid classification architecture. The system detects individual linguistic markers and aggregates them into a cumulative behavioural profile, combining the precision of rule-based forensic search with the contextual recall of a BERT-based model. Acknowledging the strict ethical and privacy constraints that limit access to real-world forensic datasets, the model was initialised on a synthetic dataset and validated using a ‘Hybrid Injection’ stress-test, embedding 200 confirmed real-world toxic samples into the forensic timeline. In comparative experiments, the hybrid model achieved a 0.85 macro F1-score, significantly outperforming standard forensic keyword search baselines. We demonstrate the pipeline’s engineering utility on a simulated 8451-message case file. By isolating the Person Under Investigation (PUI), the system generated an auditable, quantitative triage report that reduced the target’s review volume by 92.8%. While further validation on diverse real-world data is required, this work proposes a foundational ‘warm-start’ framework for AI-assisted lead generation, prioritising explainability and investigator oversight over autonomous decision-making. The Digital Conversation Analysis Pipeline (DCAP) workflow. The system automates the triage of digital evidence through three stages: (1) Ingestion & Structuring of raw forensic exports; (2) Text Analysis using a hybrid 9-trait classifier to detect markers of coercive control; and (3) Synthesis into an interpretable triage report for Human-in-the-Loop review. • A novel forensic pipeline (DCAP) for triaging text-based coercive control evidence. • Hybrid 9-trait classifier outperforms keyword search. • Quantitative triage report reduces manual review volume by over 92%. • A warm-start transfer learning framework addresses forensic data scarcity.