From moderation decision to appeal — Technical https://thesis.uya.no/proposals/from-moderation-decision-to-appeal/#technical-track BACHELOR Final result: Working ETL, automated validation suite, source-linked dashboard and defect list. Task: Build a data-quality dashboard for official DSA moderation records. Data: 20,000 DSA statements of reasons (public) Sample 10,000 records per platform from one fixed 30-day period through the official Research API. Requires: Confirm API limits and current schema; one independent reviewer for the 100-record check. Method: Use 20,000 records from two platforms over the same 30-day period. Run schema, missingness, timing and cross-field checks; manually verify 100 stratified records. STEPS Build and test 1. Read the starting sources and choose one established implementation method. 2. Write the requirements, data fields, system diagram and test cases. 3. Prepare 20,000 DSA statements of reasons. Make the answer key and pass criteria before testing. 4. Build a working version of working etl, automated validation suite, source-linked dashboard and defect list. 5. Run function, integration and failure-case tests. Record each result. 6. Run the practical evaluation and list the changes the system still needs. MASTER Final result: Versioned ETL, validation rules, source-linked audit dashboard and data-quality report. Research question: Design and evaluate an audit method that distinguishes formal schema compliance from records that actually support scrutiny and appeal. Research result: A validated audit method, cross-platform evidence on reporting quality and design requirements for contestable moderation records. STEPS Design, build and test (DSR) 1. Read the newest papers and list the closest existing systems. 2. Write down the versions, fields, data split, case assignment, random seeds and correct answers for 100,000 DSA statements of reasons from four platforms. 3. Draw the user workflow, data model and system architecture. List the requirements and pass criteria. 4. Build a working version of versioned etl, validation rules, source-linked audit dashboard and data-quality report. 5. Test every function, connection and failure case. Save the failed tests as well as the passed tests. 6. Compare the system with the named alternative. Then run the user task or decision task in the assignment. 7. Report the measured result, the failed cases and the design lessons another team can reuse. CURRENT PROJECT LITERATURE Automated Transparency: A Legal and Empirical Analysis of the Digital Services Act Transparency Database (2024, peer-reviewed conference paper): https://doi.org/10.1145/3630106.3658970 Provides the closest large-scale audit and identifies discretion and compliance limits in the database. Outside the Black Box: From Algorithmic Transparency to Platform Observability in the Digital Services Act (2024, peer-reviewed journal article): https://ojs.weizenbaum-institut.de/index.php/wjds/article/view/4_2_3 Explains why disclosure must be evaluated by what it lets outsiders observe and scrutinise. IS THEORY STARTING POINTS Kaushal et al. (2024) — Automated Transparency: https://doi.org/10.1145/3630106.3658970 Use its legal and empirical audit as the closest comparator. Leerssen (2024) — Outside the Black Box: https://ojs.weizenbaum-institut.de/index.php/wjds/article/view/4_2_3 Separate formal transparency from practical platform observability. Search Scopus or Web of Science and ACM Digital Library using the topic query, then follow citations to the thesis start date. Record searches and compare methods, data, findings and limitations in literature-matrix.csv. Use that review to confirm or revise the gap and choose a current comparator. The linked papers are starting points. PILOT QUESTION Which reporting choices make automated moderation decisions observable and contestable across platforms? EXAMPLE TOOLS Python, httpx, pandas or Polars, Pandera, DuckDB, Plotly Dash or Streamlit Equivalent tools are fine. FIRST THREE ACTIONS 1. Read the API and schema; save one small response with retrieval time and query before defining checks. 2. Name four platforms and one 60-day window; finalise fields, allowed values, exclusions and the 400-record audit sample. 3. Implement schema and cross-field rules before aggregation; make every chart trace back to record UUIDs and source permalinks. LITERATURE SEARCH DSA transparency database statement of reasons data quality automated moderation audit appeal DATA 100,000 DSA statements of reasons from four platforms (public) Use the official Research API to sample 25,000 records per named platform from the same fixed 60-day period. Store record UUID, platform, decision dates, restriction, content type, legal/terms ground, category, automation flags and source permalink. The database contains reporting metadata only. DSA Transparency Database Research API: https://transparency.dsa.ec.europa.eu/page/research-api DSA Transparency Database documentation: https://digital-strategy.ec.europa.eu/en/faqs/dsa-transparency-database-questions-and-answers HOW TO TEST IT Pin API query, schema and retrieval date. Validate completeness and allowed combinations; hand-check a stratified sample of 400 records. Compare rule-only quality scores with a simple anomaly detector as a secondary method. Report missingness, invalid-combination rate, category entropy, reporting delay and automation-use differences with platform-level confidence intervals. Treat each record as a platform report and verify the fields that can be checked against the published schema. FINAL RESULT Versioned ETL, validation rules, source-linked audit dashboard and data-quality report. ACCESS OR PEOPLE Confirm current API limits and data-retention window; independent review of 400 sampled records. TEMPLATES https://thesis.uya.no/starters/from-moderation-decision-to-appeal/technical/data-plan.csv https://thesis.uya.no/starters/from-moderation-decision-to-appeal/technical/evaluation.csv https://thesis.uya.no/starters/from-moderation-decision-to-appeal/technical/literature-matrix.csv Use participant codes instead of names or email addresses.