wikipedia-en-article-audit

You can automate a Wikipedia article audit by running a three-phase process that diagnoses factual errors, verifies structural issues, and generates a task graph for follow-up correction using the Wikipedia and Wikidata APIs.

Automated fact checking and NPOV analysis scans articles for unsupported claims, factual errors, missing infoboxes, incomplete categories, and neutral point of view violations, producing a structured task graph for corrective action.

Yes, you need Python 3.10 or higher along with the requests and mwparserfromhell dependencies to parse Wikipedia article structure and access the required APIs for auditing.

Yes, the audit process generates a task graph during its third phase, which outlines the specific structural, factual, and NPOV corrections needed for automated follow-up on the Wikipedia article.

The best way to verify infobox completeness and category presence is to run an automated structural analysis that parses the article markup and checks for missing or incomplete structural elements.

Audit limitations depend on access to Wikipedia and Wikidata APIs for claim verification, requiring proper Python environment setup with mwparserfromhell to accurately parse and identify NPOV violations within the article text.