Contribute to MuLCAM

MuLCAM is an open, community-driven effort to improve online safety in Nepali and other local languages. By contributing, you help build a shared public resource that supports research, moderation, advocacy, and platform accountability.

Contributions to MuLCAM focus on language patterns, not individuals. We collect examples of how harm, harassment, and abuse appear in digital spaces — so they can be better understood, detected, and addressed.

What You Can Contribute

You can contribute in the following ways:

  • Annotated text examples — Share short text examples and label them based on the type of harm or context they represent.
  • Lexicon suggestions — Suggest derogatory or harmful terms that should be reviewed for inclusion in the project's lexicon.

All contributions are reviewed before being added to the dataset.

How the Contribution Process Works

  1. You submit an example or suggestion using the form on this page.
  2. Your submission is stored in a review queue.
  3. Project validators review the submission for clarity, relevance, and consistency.
  4. Approved entries are added to the public dataset in future releases.

This review process helps maintain data quality and protects against misuse.

Who Can Contribute

You do not need to be a researcher or technologist to contribute.

We welcome:

  • Community members
  • Researchers and students
  • Moderators and digital safety practitioners
  • Feminist and rights-based organizations
  • Language experts and activists

If you encounter harmful or coded language online, your perspective is valuable.

Safety and Ethics

This project involves sensitive content related to online abuse and gender-based violence.

Please:

  • Avoid sharing real names, usernames, or identifiable information
  • Do not submit content that targets private individuals
  • Focus on representative examples rather than personal experiences

Contributions are anonymous by default.

Prefer to Contribute via GitHub?

If you are comfortable working with datasets, annotations, or version control, you can contribute directly on GitHub.

Direct GitHub contributions are encouraged for:

  • Bulk dataset improvements
  • Annotation corrections
  • Category or schema discussions
  • Technical or research collaboration

🔗 Contribute on GitHub

Before You Begin

Please review the FAQs below to understand:

  • What makes a good submission
  • How annotations are reviewed
  • Which languages and categories are supported

When ready, switch to the "Submit Contribution" tab to submit your contribution.

Contribution FAQs