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Deep Fig Research Lab
Behavioral Signal Intelligence & Thick Data Analytics
A scholarly program for language-based decision evidence

Our Mission
Deep Fig is a research initiative focused on extracting auditable, reproducible signals from natural language data (e.g., reviews, interviews, service conversations, internal communications). Our work emphasizes method transparency, validation discipline, and ethical governance. We publish protocols, technical reports, and research artifacts designed to be inspected, challenged, and improved.
Research Focus
What we study
  • How language encodes trust, intent, risk, and cultural norms
  • How narratives form and spread inside markets and organizations
  • How prior language frames influence subsequent language behavior
What we produce
  • Methods and protocols
  • Technical reports and working papers
  • Datasets and documentation
  • Tools: taxonomies, rubrics, evaluation scripts
Research Themes
Trust & Credibility Signals in Reviews
Conversation Dynamics in Sales & Service
Culture, Leadership, and Alignment Narratives
Risk Discourse & Incident Language
Decision Framing in Organizations
Cross-cultural Language Variance
Featured Research
Trust & Credibility Signals in Reviews
Core question:
What linguistic signals correlate with perceived credibility and downstream decision impact?
Typical data:
public review corpora, verified-purchase reviews, longitudinal review threads
Measures:
stance, certainty language, specificity, causal explanations, temporal anchoring
Limitations:
platform bias, moderation effects, selection bias, domain dependence
Primary outputs:
annotated corpora, scoring rubrics, replication scripts, technical reports
Research Methodology
Data → Preparation → Signal Extraction → Modeling → Validation → Reporting → Archiving
Preparation
  • Cleaning, de-identification, segmentation
  • Metadata normalization (time, source, role)
Signal Extraction
  • Lexical/semantic markers
  • Discourse structure (claims, evidence, hedges)
  • Narrative patterns and framing
Validation
  • Baselines, ablations where feasible
  • Inter-annotator agreement (when annotation used)
  • Error analysis + failure mode catalog
Core Principles
Explainability
every insight must map to observable evidence
Reproducibility
protocols and versions are explicit
Constraint discipline
we do not infer beyond the data's warrant
Bias awareness
we document dataset limitations and known skews
Ethical handling
privacy-first, minimal retention, controlled access
Research Outputs
Research outputs
  • Working papers / technical reports
  • Protocols and taxonomies
  • Replication packages
  • Dataset documentation ("data statements")
Decision artifacts
  • Evidence tables (signal → examples → coverage)
  • Risk register (risk → trigger language → mitigation)
  • Narrative maps (dominant frames and counter-frames)
Datasets & Documentation
Dataset Name (v1.0)
Scope
Details the domain and time range covered by the dataset.
Source Policy
Specifies whether the data is public, licensed, or synthetically generated.
Collection Method
Describes the methodology and process used for gathering the data.
Anonymization
Explains what identifying information is removed and the techniques employed.
Known Skews
Identifies potential biases related to sampling, geographical region, or platform.
Access
Indicates whether data access is open, restricted, or by request.
Documentation
Includes a comprehensive data dictionary and a detailed labeling guide.
Ethics & Governance
Ethical commitments
  • Consent and provenance accountability
  • PII minimization and redaction-by-default
  • Purpose limitation (no secondary misuse)
  • Human review for sensitive interpretations
What we will not do
  • No individualized diagnosis or mental health inference
  • No identity guessing
  • No "black-box" conclusions without evidence anchors
Deep Fig Research Lab
Method-led. Evidence-anchored. Ethically governed.
Decode. Decide. Deliver.
Deep Fig Research Lab investigates how language encodes trust, intent, risk, and culture across markets and organizations. We publish transparent methods, technical reports, datasets, and tools designed for reproducibility and ethical use. Our work prioritizes evidence anchoring, explicit limitations, and governance-first data handling.
Contact
Benedict Gnaniah
+917010123203 or +919962957037