Audit what a feed amplifies — Non-technical
https://thesis.uya.no/proposals/audit-what-a-feed-amplifies/#nontechnical-track

BACHELOR
Final result: Comparison table, evidence gaps and a plain-language disclosure template.
Task: Compare what four platforms tell users about why content is recommended and which controls users receive.
Data: 12 public recommender documents from four platforms (public)
Collect one recommender description, one user-control page and one policy/transparency document per platform. finalise date, URL and PDF.
Requires: No participant recruitment; agree the four platforms and document date with the supervisor.
Method: Code 12 fixed public documents with an established transparency checklist. Double-code 3 documents and report differences by platform and document type.

STEPS
Study and improve
1. Read the starting sources and write the exact information problem.
2. Prepare 12 public recommender documents from four platforms and a separate answer sheet or coding sheet.
3. Run one pilot and fix unclear questions.
4. Collect the named evidence with consent.
5. Group the findings with the stated categories and check the answer sheet.
6. Produce comparison table, evidence gaps and a plain-language disclosure template. List the three most useful changes.

MASTER
Final result: A source table for each platform, a platform-by-platform comparison and a disclosure template for recommender audits.
Research question: Explain how platform disclosures make recommender risk more or less observable to researchers, regulators and prevention professionals.
Research result: A cross-platform observability framework and evidence-based minimum disclosure requirements for recommender oversight.

STEPS
Study, compare and explain
1. Read the newest papers and write one exact research question.
2. State which people, cases or documents you will study and what you will compare.
3. Write the selection rules, questions and analysis steps for 24 public platform and DSA documents + 8–10 professional interviews.
4. Run one pilot. Fix unclear questions or categories, then keep the guide unchanged.
5. Collect the named interviews, cases or documents with consent.
6. Analyse them with the stated comparison or coding method. Keep disagreements and missing data.
7. Report the answer, the evidence and the practical output named in the assignment.

CURRENT PROJECT LITERATURE
Causally estimating the effect of YouTube’s recommender system using counterfactual bots (2024, peer-reviewed journal article): https://pmc.ncbi.nlm.nih.gov/articles/PMC10895271/
Separates recommender effects from user preferences and shows why audit claims need a counterfactual design.

8–10% of algorithmic recommendations are bad, but… an exploratory risk-utility meta-analysis (2024, peer-reviewed journal article): https://doi.org/10.1016/j.ijinfomgt.2023.102743
Frames recommendation design as a measurable risk–utility trade-off rather than a one-sided accuracy problem.

IS THEORY STARTING POINTS
Leerssen (2024) — Outside the Black Box: https://ojs.weizenbaum-institut.de/index.php/wjds/article/view/4_2_3
Distinguish transparency from the practical observability needed for oversight.
Gleiss et al. (2023) — Identifying the patterns of platform regulation: https://aisel.aisnet.org/jit/vol38/iss2/6/
Structure the comparison of regulatory problems and response instruments.

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
What information do professionals need to judge whether a recommender risk claim is credible?

EXAMPLE TOOLS
Zotero, LibreOffice Calc, Taguette or NVivo; no programming required.
Equivalent tools are fine.

FIRST THREE ACTIONS
1. Name the six platforms and four required document types; archive a dated copy of every source.
2. Pilot the coding matrix on two platforms and double-code four documents; revise and finalise it.
3. Prepare an oversight task and interview guide; recruit only professionals and exclude case-specific operational details.

INTERVIEW QUESTIONS
- Which disclosure would let you check a platform’s risk claim?
- What important information is missing or impossible to compare?
- Which control should belong to users, platforms or regulators?

LITERATURE SEARCH
platform recommender transparency algorithm audit governance youth extremism DSA

DATA
24 public platform and DSA documents + 8–10 professional interviews (public)
For six named platforms, collect one recommender description, one terms/policy document, one DSA risk or transparency report and one independent audit/regulator document dated 2024–2027. finalise URLs and PDFs before coding. Interview researchers, prevention professionals or regulators—not young people or platform users.
European Commission DSA transparency overview: https://digital-strategy.ec.europa.eu/en/policies/dsa-brings-transparency

HOW TO TEST IT
Use a predefined document-coding matrix and double-code 20% of documents. Compare stated objectives, risk measures, user controls and evidence across platforms. Use the interviews to test where disclosures support or block an oversight task; retain other explanations and negative cases.

FINAL RESULT
A source table for each platform, a platform-by-platform comparison and a disclosure template for recommender audits.

ACCESS OR PEOPLE
Recruit 8–10 relevant professionals; obtain consent and avoid operationally sensitive case information.

TEMPLATES
https://thesis.uya.no/starters/audit-what-a-feed-amplifies/nontechnical/data-plan.csv
https://thesis.uya.no/starters/audit-what-a-feed-amplifies/nontechnical/evaluation.csv
https://thesis.uya.no/starters/audit-what-a-feed-amplifies/nontechnical/literature-matrix.csv
Use participant codes instead of names or email addresses.