Timeline Analysis
By Seme Research Team · Updated May 22, 2026
Definition
Timeline Analysis is the process of organizing discovered facts chronologically to create a coherent narrative of a subject's life events. In identity investigation, timeline analysis transforms scattered data points from multiple sources into a structured chronological sequence, helping investigators identify gaps, inconsistencies, and patterns that may require further investigation. A well-constructed timeline includes dated events (employment start/end, education enrollment/graduation, publication dates, travel events), source citations for each event, and confidence indicators. Timeline analysis is particularly valuable for verifying alibis, detecting fabrication (events that don't fit the timeline), and identifying periods of unexplained activity or inactivity.
How It Works
Timeline construction follows a four-phase process. Phase 1 — Event Extraction: NLP models parse collected evidence to identify date-stamped events (employment records, publication dates, social media posts with timestamps, travel records). Phase 2 — Normalization: converting dates to a standard format, resolving ambiguous dates ("early 2020" → "2020-Q1"), and handling different calendar systems. Phase 3 — Sequencing: arranging events chronologically and identifying overlaps, gaps, and concurrent activities. Phase 4 — Analysis: flagging anomalies (gaps longer than 6 months, overlapping employment at different locations, impossible date combinations) and generating investigation questions for unexplained periods.
Example
Constructing a timeline for a job candidate reveals: 2015-2018: Software Engineer at Company A (E1 — HR records). 2018-2019: 6-month gap (flagged for investigation). 2019-2022: Senior Engineer at Company B (E1 — LinkedIn + company records). 2022-present: CTO at Startup C (E2 — press release). Analysis flags: the 6-month gap between Company A and Company B (candidate claims "traveling" but no social media posts corroborate this), and Startup C was incorporated 3 months before the candidate claims to have joined (potential founding date discrepancy).
Applications
- •Employment history verification for hiring decisions
- •Alibi verification in legal investigations
- •Activity pattern analysis for due diligence
- •Chronological evidence presentation for legal proceedings
Key Statistics
| Metric | Value | Source |
|---|---|---|
| Event Categories | 6+ types | Seme schema |
| Gap Detection Threshold | 6 months | Analysis config |
| Anomaly Detection Accuracy | 91% | Internal benchmarks |
| Timeline Resolution | Day-level precision | Seme methodology |