Forensic Psycholinguistic Stream Analysis

• Forensic psycholinguistic stream is a paradigm that uses explicit, quantifiable psycholinguistic cues to detect deception and investigative behavior.
• It leverages engineered features—from lexical to affective and syntactic markers—coupled with cost-sensitive statistical models for real-time forensic screening.
• Its transparent and interpretable approach enables efficient profiling in applications like business email compromise, cyber threat attribution, and forensic interviewing.

Forensic psycholinguistic stream refers to a methodological paradigm in computational forensics that detects, profiles, or assesses deceptive, malicious, or otherwise investigatively relevant behaviors by leveraging quantifiable psycholinguistic cues—lexical, affective, syntactic, pragmatic—extracted from natural language artifacts. This paradigm is operationalized through engineered feature sets, cost-sensitive statistical modeling, and interpretability-focused workflows optimized for forensic deployment. Applications encompass business email compromise, cyber threat attribution, forensic interviewing, and investigative triage across multiple languages and modalities.

1. Theoretical Foundations and Scope

The forensic psycholinguistic stream fundamentally targets the quantification of latent psychological, pragmatic, and intentional properties embedded in linguistic production. Unlike semantic deep learning approaches that model contextual meaning in high-dimensional neural space, this stream utilizes explicit, interpretable features informed by psychological theory, social science (e.g., Cialdini’s persuasion principles), and forensic linguistic conventions. It aims to capture not only surface-level content words or n-grams, but also stylometric, pragmatic, and discourse-level signals of intent, rapport, affect, or deception. Representative case domains include criminal deception (fake news, fraudulent communications) (Vargas et al., 2020), cyber threat and social engineering (business email compromise, phish attribution) (Adjei, 26 Nov 2025), and investigative interviewing (child forensic interviews) (Ardulov et al., 2018).

2. Psycholinguistic Feature Engineering

A core distinguishing attribute of the forensic psycholinguistic stream is the breadth and granularity of feature engineering, spanning:

• Persuasion and Social Engineering Cues: Authority, scarcity, reciprocity, liking, social proof, commitment, and urgency, systematically counted via lexical patterns and semantic frames (e.g., AuthorityCueCount, ScarcityCueCount).
• Affective and Sentiment Metrics: Polarity, subjectivity, and fine-grained emotion categories (Joy, Sadness, Anger, Fear), extracted through specialized lexicons (e.g., Sentilex-PT, VADER, WordNetAffect.BR) (Vargas et al., 2020).
• Formal Linguistic Properties: Pronoun distribution (first/second/third person, subject/oblique), part-of-speech frequencies (notably INTJ use for interjections), modal/auxiliary verb counts, clause/sentence structure (PassiveVoiceRatio, ConditionalClauseCount) (Vargas et al., 2020, Adjei, 26 Nov 2025).
• Pragmatic/Discourse Features: Hedges, boosters, disfluencies, exclusives, apology markers, politeness strategies, and stylistic markers of indirectness or confrontation (Adjei, 26 Nov 2025).
• Structural/Textual Artifacts: Uppercase/lowercase ratio, punctuation profiles (notably elevated “!”, “?”, ellipsis, quotation marks in fake/deceptive news), special characters, average word/sentence length, entity mention counts (PER, ORG, LOC, MISC) (Vargas et al., 2020, Adjei, 26 Nov 2025).

An illustrative summary for business email compromise detection (Adjei, 26 Nov 2025):

Feature Group	Examples	Quantified As
Persuasion cues	AuthorityCueCount, ScarcityCueCount	Token/phrase counts
Sentiment/affect	PolarityScore, SmilingAssassinScore	Scores from lexica/ML models
Structural	CapsRatio, URLCount, ExclamationCount	Ratios, absolute counts
Linguistic style	HedgesCount, PassiveVoiceRatio	Frequency in normalized form

Statistical analysis of these features underlies forensic signal extraction and model discrimination between deceptive/non-deceptive or benign/malicious artifacts.

3. Model Architectures and Cost-Sensitive Learning

The forensic psycholinguistic approach operationalizes its feature space via interpretable, typically tree-based statistical models such as CatBoost, Random Forests, or shallow classifiers (logistic regression, SVM). CatBoost has been highlighted for statistically robust learning on numerical and categorical features with efficient handling of high-cardinality tabular data, leveraging ordered target statistics for categorical encoding (Adjei, 26 Nov 2025).

Critical to forensic utility is cost-sensitive learning. Unlike standard precision/recall-balanced objectives, forensic deployment accounts for the much greater financial or investigative loss of a false negative (e.g., allowing a fraudulent email through) compared to a false positive (triggering manual review). The expected financial loss:

$$E\left[L_{\text{fin}}\right]=\sum_{i=1}^N \left[\mathbb{I}_{\mathrm{FN},i}\,V_i+\mathbb{I}_{\mathrm{FP},i}\,C_{\text{inv}}+\mathbb{I}_{G,i}\,C_{\text{rev}}\right]$$

Here, $V_i$ is fraud loss, $C_{\text{inv}}$ the cost of human investigation, and the operational threshold $\tau$ is optimized to minimize $E[L_{\text{fin}}]$ rather than log-loss (Adjei, 26 Nov 2025).

4. Empirical Performance and Interpretability

Benchmark results for forensic psycholinguistic streams indicate near-state-of-the-art accuracy, but with vastly improved inference speed and transparency compared to deep, semantic models:

Model	AUC	F1	Latency (ms/email)
CatBoost (Psycholing)	0.9905	0.9486	0.885
DistilBERT (Semantic)	1.0000	0.9981	7.403

The CatBoost stream enables real-time screening (sub-ms latency) and explicability through SHAP value analysis. Salient features influencing output include authority/urgency cues, sentiment metrics (ψ Score), and monetary/technical term presence (Adjei, 26 Nov 2025). Forensic statement analysts and investigators gain direct, interpretable insight into which linguistic markers drive a suspicious verdict, a key requirement for high-stakes legal and operational settings.

5. Forensic Psycholinguistic Analysis in Applied Contexts

5.1. Business Email Compromise

The forensic psycholinguistic stream excels in BEC triage due to its low operational cost, interpretability, and ability to model psychological manipulation patterns. It addresses economic asymmetry (the loss function prioritizes $V_i \gg C_{\text{inv}}$) and produces a return on investment (ROI) exceeding 99.96% under cost-sensitive optimization (Adjei, 26 Nov 2025).

5.2. Forensic Interviewing

In forensic interviews with children, information retrieval effectiveness is measured via productivity and responsiveness metrics—quantifying the degree to which a child's response aligns with the interviewer’s topical agenda and entrains to linguistic cues (Ardulov et al., 2018). Metrics such as $g(r_t)=\vec{r}_t\cdot A_{(\Psi)}$ (agenda alignment) and $\rho(r_t)=\vec{r}_t\cdot a_t$ (entrainment) provide sparse, objective signals for substantive disclosures. These metrics correlate weakly with age (Pearson r ≈ 0.24–0.26 vs. 0.46 for raw word count), reflecting sensitivity to informational value over verbosity.

5.3. Deceptive Intent in Multilingual Media

Empirical studies of fake news in Brazilian Portuguese reveal language- and genre-specific deception markers. Fake statements exhibit pronounced increases in sentiment-bearing words (+~12%), interjections (+333%), expressive punctuation, and distinctive pronoun usage compared to true news (Vargas et al., 2020). These features translate into formal triage tools adaptable to domain shifts and cross-linguistic validation.

6. Model-Driven Profiling and Future Directions

Integrating engineered psycholinguistic features with modern LLMs in hybrid architectures further enhances profiling efficacy. Feature fusion practices (late concatenation of psycholinguistic vectors $f\in \mathbb{R}^d$ and LLM embeddings $e\in \mathbb{R}^D$) have been shown to provide superior trait estimation and classification performance (F1=0.86, ROC AUC=0.91 for the unified model; MAE=0.12 for trait scoring vs. LLM-only MAE=0.18) (Tshimula et al., 26 Jun 2024).

Identified limitations include data bias, domain drift, and the need for regular retraining to track shifting social engineering tactics. Ethical use mandates compliance with privacy regulations (e.g., GDPR), transparency logging, and human-in-the-loop validation. Future research will incorporate multimodal streams (voice, keystroke dynamics), expand to cross-lingual pipelines, develop adversarial robustness, and pursue more intensive human–AI collaborative interfaces (Tshimula et al., 26 Jun 2024).

7. Interpretability, Deployment, and Limitations

The forensic psycholinguistic stream is optimized for CPU-only, scalable operation, facilitating high-throughput deployment in security gateways, fraud management, and investigative casework. Its main advantages are interpretability (feature importance, auditability), low latency, and direct alignment to psychological constructs familiar to investigators. Its chief trade-offs are brittleness on ultra-short or obfuscated inputs and slightly reduced detection ceiling compared to full semantic models. This suggests adoption should be context-specific: edge or high-throughput systems benefit most from this stream, while environments demanding maximal semantic robustness may prefer deep learning alternatives with hybrid psycholinguistic augmentation (Adjei, 26 Nov 2025).