Frequently Asked Questions

It's built for HASS researchers working with short-video data donations: people who want to study lived platform experience from participants' own feeds without spending months reconciling export formats, recovering stripped-out metadata and hand-coding thousands of videos. The Hub automates that pipeline so you can get to analysis.

Possibly. We're interested in working with other research teams. The Hub supports bespoke annotation prompts per project and role-based access for collaborators and students. Get in touch at info@foryouresearch.net to discuss your project.

Yes to both. The Hub is released under the MIT licence and developed in the open at github.com/pwikstrom/foryou-research. Everything you see here (ingestion, enrichment, annotation and the dashboard) is in that repository, and you are free to run it, fork it and adapt it for your own project.

Running your own instance means you keep your participants' data on infrastructure you control, under your own ethics approval. Start with the installation guide; the Hub runs locally on a laptop for small studies and on Google Cloud Run for larger ones.

Register on the sign-up page with your email address. New accounts are reviewed by an administrator before they can log in, so you may see "pending approval" for a short while. The hub is intended for researchers connected to the For You Research Project. If you'd like access, get in touch at info@foryouresearch.net.

The hub holds short-video activity data donated by study participants (TikTok feed activity, Instagram activity and YouTube watch history), enriched with public metadata about the videos that appear in those feeds (captions, creators, popularity) and with AI-generated content annotations (categories, themes, style and more).

Participation is voluntary and participants choose what data to contribute. Donations are handled under the project's research ethics protocols, access to the hub is restricted to approved accounts with role-based permissions, and analysis views are designed around aggregate patterns rather than exposing individuals.

Yes. The project is actively looking for TikTok users and creators to take part. See how to participate. Data donors also receive insights back into their own platform activity.

TikTok is the project's focus, and the hub also ingests donated Instagram activity and YouTube watch history, so recommendation dynamics can be compared across platforms.

Video annotations are generated by a large multimodal AI model against a structured, versioned codebook, and the team continuously evaluates annotation quality, including comparisons with human coders. Still, some outputs may be imperfect or unexpected; treat individual annotations with appropriate care and look at aggregate patterns where possible.

Please acknowledge The For You Research Project and its funding: Australian Research Council grant DP240102939, Queensland University of Technology and the University of Sydney. For citation formats or co-authorship questions, contact the team at info@foryouresearch.net.

To cite the software itself, use the "Cite this repository" button on the GitHub repository. It reads the repository's CITATION.cff, which carries the archived Zenodo DOI for each release.

No. TikTok is a registered trademark of Bytedance Ltd. This website and the research project are not affiliated with or endorsed by TikTok or Bytedance Ltd.

The hub is under active development, so bugs happen. Bug reports and feature requests belong in the public issue tracker: browse the open issues first in case yours is already known, then open a new issue saying what you were doing, what you expected and what happened instead. Screenshots help a lot.

Two things that don't belong in an issue, because issues are public: participant data (including screenshots that identify a participant) and suspected security problems. Report the latter privately, following the security policy. For anything else you'd rather not post publicly, email info@foryouresearch.net.

Please do. The contributing guide covers the branch workflow, the test suite and the conventions the codebase relies on. Adding support for another platform is deliberately small (one ingestion class and one scraper class), and bug fixes, documentation and test coverage are just as welcome. If you're planning something substantial, open an issue first so we can talk it through before you write the code.