An evolution of forensic linguistics: From manual analysis to machine learning – A narrative review

Abstract

Forensic linguistics has evolved from manual textual analysis to machine learning (ML)-driven methodologies, fundamentally transforming its role in criminal investigations. This narrative review clarifies three core objectives: (1) tracing the field’s historical trajectory from early manual techniques to computational innovations, (2) systematically comparing the accuracy, efficiency, and reliability of manual versus ML-based approaches, and (3) identifying persistent challenges in ML integration, including algorithmic bias and legal admissibility. By synthesizing 77 studies, the analysis reveals that ML algorithms—notably deep learning and computational stylometry—outperform manual methods in processing large datasets rapidly and identifying subtle linguistic patterns (e.g., authorship attribution accuracy increased by 34 % in ML models). However, manual analysis retains superiority in interpreting cultural nuances and contextual subtleties, underscoring the need for hybrid frameworks that merge human expertise with computational scalability. The study’s novel contribution lies in its empirical demonstration of ML’s transformative potential while critiquing overreliance on automated systems without ethical safeguards. Key challenges, such as biased training data and opaque algorithmic decision-making, highlight unresolved barriers to courtroom admissibility. The review concludes by advocating for standardized validation protocols and interdisciplinary collaboration to advance forensic linguistics into an era of ethically grounded, AI-augmented justice. This dual emphasis on technological innovation and critical oversight positions the field to address evolving demands for precision and interpretability in legal evidence analysis. By addressing these issues, the field is well-positioned to advance as an indispensable and ethically grounded tool in pursuing justice.