# Web Scraping for Research: Legal, Ethical, Institutional, and Scientific Considerations

arXiv: https://arxiv.org/abs/2410.23432 · HTML: https://mlsanity.com/paper/2410.23432/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2024.

## Built on by (most cited first, 4 of 4)

- [Neglected Risks: The Disturbing Reality of Children's Images in Datasets and the Urgent Call for Accountability](https://mlsanity.com/paper/2504.14446/) (2025)
- [Permission Manifests for Web Agents](https://mlsanity.com/paper/2601.02371/) (2025)
- [PETLP: A Privacy-by-Design Pipeline for Social Media Data in AI Research](https://mlsanity.com/paper/2508.09232/) (2025)
- [From Patent Expiry to Business Pathways: AI Workflows for Activating Innovation Archives](https://mlsanity.com/paper/2607.10179/) (2026)

## Abstract (arXiv)

Scientists across disciplines often use data from the internet to conduct research, generating valuable insights about human behavior. However, as generative AI relying on massive text corpora becomes increasingly valuable, platforms have greatly restricted access to data through official channels. As a result, researchers will likely engage in more web scraping to collect data, introducing new challenges and concerns for researchers. This paper proposes a comprehensive framework for web scraping in social science research for U.S.-based researchers, examining the legal, ethical, institutional, and scientific factors that researchers should consider when scraping the web. We present an overview of the current regulatory environment impacting when and how researchers can access, collect, store, and share data via scraping. We then provide researchers with recommendations to conduct scraping in a scientifically legitimate and ethical manner. We aim to equip researchers with the relevant information to mitigate risks and maximize the impact of their research amidst this evolving data access landscape.