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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