MWI is a reproducible research toolkit to collect web corpora, qualify/enrich them (NLP/LLM-assisted, auditable), and export interpretable outputs (CSV/JSON/GEXF) for digital methods in social sciences and communication studies.

Use this repository first:

• mwi (local reproducible “desktop-lab”): https://github.com/MyWebIntelligence/mwi

Do not start with mywebapi unless you explicitly need a scalable backend.

Recommended: Docker Compose.

git clone https://github.com/MyWebIntelligence/mwi.git
cd mwi

# Choose one mode
./scripts/docker-compose-setup.sh basic   # minimal local setup
# ./scripts/docker-compose-setup.sh api   # API-oriented mode
# ./scripts/docker-compose-setup.sh llm   # ML/embeddings/LLM mode

# Sanity check (example command)
docker compose exec mwi python mywi.py land list

Full installation details:

• https://github.com/MyWebIntelligence/mwi
• https://github.com/MyWebIntelligence/mwi/blob/master/docs/INSTALL_ZERO_bis.md

Collect → Qualify → Analyze → Export

1. Collect
   Build a corpus from seed URLs and curated sources, keep crawl traces, store pages + metadata.

2. Qualify
   Extract readable content, enrich with NLP and optional LLM-based relevance gating.
   Auditability is a design goal: raw traces are kept and decisions can be inspected.

3. Analyze
   Produce socio-semantic structures: documents, expressions/entities, similarity links, networks.

4. Export
   Generate outputs for analysis and visualization:

• CSV / JSON
• GEXF (Gephi)
• structured datasets / reports

A Land is a research project container (topic) holding:

• terms, seed URLs, crawls
• extracted content + metadata
• enrichment layers
• exports

Think: one Land = one case study / one dataset / one pipeline run.

• mwi
  Local reproducible research tool (Python + SQLite + Docker Compose).
  https://github.com/MyWebIntelligence/mwi

• mwiR
  R package for analysis and R-friendly workflows (bridge for R users).
  https://github.com/MyWebIntelligence/mwiR

• mywebapi
  Scalable backend (FastAPI + PostgreSQL + Celery + Redis).
  Note: this repository contains components “in transition” (API + legacy parts).
  https://github.com/MyWebIntelligence/mywebapi

        ┌──────────────────────────┐
        │ mwi (flagship, local)     │
        │ CLI + reproducible setup  │
        └─────────────┬────────────┘
                      │
          SQLite DB + corpus files
                      │
        ┌─────────────┴────────────┐
        │                          │
  Exports (CSV/JSON/GEXF)     Optional scale-out
  for R / Gephi / notebooks   (mywebapi: Postgres/API/Celery)
        │                          │
      mwiR as bridge          external clients/pipelines

Recommended practice (until stable releases are published everywhere):

1. Cite the relevant paper(s) (HAL/publications).
2. Cite the software using either:
   • a GitHub Release tag (preferred), or
   • a commit hash.

Recommended professionalization steps:

• Add CITATION.cff to mwi and mwiR
• Publish GitHub Releases (e.g., v0.1.0)
• Archive releases to Zenodo (DOI)

For research collaborations, deployments at scale, or reproducible case studies, open an issue on the flagship repository: https://github.com/MyWebIntelligence/mwi/issues

See each repository for licensing details (MIT where specified).