Forensic Behavioral Analysis
Methodology White Paper

Table of Contents

1. Overview and Intended Use
2. Input Processing and Format Support
3. Subject Identification
4. Pattern Detection Methodology
5. Warning Patterns — Definitions and Citations
6. Positive Patterns
7. Confidence Score Calculation
8. Relationship Health Score
9. Personality and Risk Assessment Frameworks
10. SARA Risk Assessment
11. Limitations and Caveats
12. Federal Rule of Evidence 702 Considerations
13. Bibliography

1.Overview and Intended Use

Verity is a forensic conversation analysis system designed to assist licensed legal professionals in documenting behavioral patterns within digital communications. The system processes exported conversation files — from platforms including WhatsApp, SMS, Facebook Messenger, Instagram, Telegram, Discord, and email (.mbox) — and applies a rule-based pattern matching engine informed by established psychological literature on coercive control, domestic abuse dynamics, and relationship communication research.

Verity reports are intended as supporting forensic documentation for use by attorneys, court-appointed advocates, and mental health professionals. They are not a substitute for expert witness testimony from a qualified forensic psychologist, clinical diagnosis, or independent professional evaluation. The existence of a detected pattern in a conversation does not, by itself, establish abuse, coercive control, or any legal finding.

2.Input Processing and Format Support

The Verity parser accepts exported conversation files in the following formats, with automatic format detection based on file extension and content structure:

Files up to 50 MB are supported. Multi-file uploads are accepted; messages from multiple files are merged and sorted by timestamp before analysis. There is no practical upper limit on message count — the engine has been tested against exports exceeding 80,000 messages.

3.Subject Identification

The analysis subject — the party whose behavioral patterns are being evaluated — is determined automatically using message volume heuristics:

1. All unique sender identities are enumerated from the conversation.
2. The sender with the fewest messages is classified as the analyzing party (the user).
3. All remaining senders are classified as the subject(s).
4. Pattern matching is applied only to the subject's messages.
5. The subject with the highest message count among non-user senders is designated the primary subject for reporting.

This heuristic assumes the party who exported the conversation sent fewer messages than the subject being analyzed. In cases where message counts are equal, the first sender encountered in the export is classified as the user. Attorneys should verify subject identification on the report cover page before use.

4.Pattern Detection Methodology

Verity uses a lexical rule-based pattern matchingapproach. For each defined behavioral pattern, a set of regular expressions is tested against each of the subject's messages. A message is flagged for a pattern if any of the pattern's expressions matches the message text.

The approach is deterministic and fully auditable — the same input will always produce the same output, and the specific phrases that triggered each pattern flag are preserved and available in the report under "Flagged Message Activity." This makes the analysis transparent and reproducible, which is important for cross-examination purposes.

The engine applies several refinements to reduce false positives:

• Minimum message length: Messages shorter than 3 characters are not evaluated.
• Stonewalling disambiguation: Short responses that match stonewalling patterns are not flagged when the preceding message was a question or request, as this indicates a genuine (if brief) reply rather than dismissive disengagement.
• Cycle-apology detection: Apologies occurring within 72 hours of a flagged pattern match are identified as potentially cyclic rather than genuine repair, and are noted separately in the report.
• Escalation analysis: Flagged messages are mapped month-by-month to identify temporal escalation in pattern frequency, a factor relevant to coercive control assessments.

Pattern matching runs entirely against the subject's messages. Messages from the user/analyzing party are used only for context (e.g., determining whether a subject's short response followed a question) and are not themselves flagged.

5.Warning Patterns — Definitions and Citations

The following 16 warning patterns are evaluated. Severity levels (Critical / High / Medium) reflect the weight of the pattern in the health score calculation and the strength of its association with abusive relationship dynamics in the research literature.

6.Positive Patterns

Seven positive communication patterns are evaluated and, when detected, contribute a modest upward adjustment to the health score. Their presence does not negate warning patterns but provides contextual information about the full communication dynamic.

7.Confidence Score Calculation

Each detected pattern is assigned a confidence score between 0.0 and 1.0 based on the ratio of flagged messages to total messages in the conversation:

Confidence scores are reported per-pattern in the Flagged Message Activity section. A low confidence score does not mean the pattern is absent — it means the detected instances represent a small proportion of the total conversation volume. Attorneys should weigh infrequent high-severity patterns (e.g., a single explicit threat) appropriately regardless of confidence score.

8.Relationship Health Score

The Relationship Health Score is a composite metric ranging from 0 to 100. It begins at 100 and applies deductions for each detected warning pattern, with partial credit for detected positive patterns.

Score interpretation:

9.Personality and Risk Assessment Frameworks

Verity incorporates two established personality assessment frameworks and one structured violence risk protocol. These are applied as supplementary analyses to the primary pattern detection.

10.SARA Risk Assessment

The Spousal Assault Risk Assessment (SARA; Kropp et al., 1994) is a structured professional judgment tool widely used by courts, law enforcement, and mental health professionals to assess risk of intimate partner violence. Verity incorporates a 10-item SARA checklist as an attorney-completed assessment — the attorney reviews the factors and checks those that apply based on their knowledge of the case, not solely from the conversation data.

SARA items included in Verity:

1. Past assault of family members
2. Past assault of strangers or acquaintances
3. Past violation of conditional release or court orders
4. Recent relationship problems
5. Recent employment problems
6. Victim of or witness to family violence as a child
7. Recent substance abuse or dependence
8. Recent suicidal or homicidal ideation or intent
9. Recent psychotic or manic symptoms
10. Personality disorder with anger, impulsivity, or instability

11.Limitations and Caveats

12.Federal Rule of Evidence 702 Considerations

Federal Rule of Evidence 702 (and analogous state rules) governs the admissibility of expert testimony based on scientific, technical, or specialized knowledge. Under Daubert v. Merrell Dow Pharmaceuticals (1993) and its progeny, courts evaluate whether methodology is: (1) testable and tested; (2) subject to peer review and publication; (3) known or potential error rate; and (4) generally accepted in the relevant scientific community.

Verity's position on each Daubert factor:

13.Bibliography