# PETLP: A Privacy-by-Design Pipeline for Social Media Data in AI Research

arXiv: https://arxiv.org/abs/2508.09232 · HTML: https://mlsanity.com/paper/2508.09232/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 78 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Scalable Extraction of Training Data from (Production) Language Models](https://mlsanity.com/paper/2311.17035/) (2023)
- [Extracting Cultural Commonsense Knowledge at Scale](https://mlsanity.com/paper/2210.07763/) (2022)
- [A Synthetic Dataset for Personal Attribute Inference](https://mlsanity.com/paper/2406.07217/) (2024)
- [Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications](https://mlsanity.com/paper/1910.12611/) (2019)
- [Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?](https://mlsanity.com/paper/2404.12691/) (2024)
- [Law and the Emerging Political Economy of Algorithmic Audits](https://mlsanity.com/paper/2406.11855/) (2024)
- [Publishing Wikipedia usage data with strong privacy guarantees](https://mlsanity.com/paper/2308.16298/) (2023)
- [Web Scraping for Research: Legal, Ethical, Institutional, and Scientific Considerations](https://mlsanity.com/paper/2410.23432/) (2024)
- [A Peek into the Political Biases in Email Spam Filtering Algorithms During US Election 2020](https://mlsanity.com/paper/2203.16743/) (2022)
- [Tight Competitive and Variance Analyses of Matching Policies in Gig Platforms](https://mlsanity.com/paper/2401.13842/) (2024)
- [Utilizing Large Language Models to Identify Reddit Users Considering Vaping Cessation for Digital Interventions](https://mlsanity.com/paper/2404.17607/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Privacy by Design Framework for Large Language Model-Based Applications for Children](https://mlsanity.com/paper/2602.17418/) (2026)

## Abstract (arXiv)

Social media data presents AI researchers with overlapping obligations under the GDPR, copyright law, and platform terms -- yet existing frameworks fail to integrate these regulatory domains, leaving researchers without unified guidance. We introduce PETLP (Privacy-by-design Extract, Transform, Load, and Present), a compliance framework that embeds legal safeguards directly into extended ETL pipelines. Central to PETLP is treating Data Protection Impact Assessments as living documents that evolve from pre-registration through dissemination. Through systematic Reddit analysis, we demonstrate how extraction rights fundamentally differ between qualifying research organisations (who can invoke DSM Article 3 to override platform restrictions) and commercial entities (bound by terms of service), whilst GDPR obligations apply universally. We demonstrate why true anonymisation remains unachievable for social media data and expose the legal gap between permitted dataset creation and uncertain model distribution. By structuring compliance decisions into practical workflows and simplifying institutional data management plans, PETLP enables researchers to navigate regulatory complexity with confidence, bridging the gap between legal requirements and research practice.