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

BACHELOR
Final result: Working simulator, three rankers, automated tests and comparison dashboard.
Task: Build a replayable feed and compare chronological, engagement-only and diversity-constrained ranking.
Data: 400-item controlled feed benchmark (planned)
Create neutral proxy content across 8 topics and 20 sources, plus 50 profiles. finalise all labels and generator settings before comparison.
Requires: Independent review of the synthetic labels and scenarios.
Method: Use 400 synthetic items and 50 fixed profiles. Run 10 sessions per profile with fixed seeds; report source concentration, topic diversity, NDCG@10 and invariant-test results.

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 400-item controlled feed benchmark. Make the answer key and pass criteria before testing.
4. Build a working version of working simulator, three rankers, automated tests and comparison dashboard.
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: Deterministic feed simulator, five rankers, audit dashboard, benchmark and policy trade-off report.
Research question: Design and evaluate a ranking policy that makes exposure risk auditable while preserving relevance and viewpoint/source diversity.
Research result: A reproducible offline audit method and evidence about the trade-offs between engagement, relevance, diversity and risky exposure.

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 1,200-item controlled feed benchmark.
3. Draw the user workflow, data model and system architecture. List the requirements and pass criteria.
4. Build a working version of deterministic feed simulator, five rankers, audit dashboard, benchmark and policy trade-off 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
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
Haroon et al. (2024) — Causally estimating the effect of YouTube’s recommender system: https://pmc.ncbi.nlm.nih.gov/articles/PMC10895271/
Ground causal claims and distinguish user choice from recommender effects.
Hoffmann et al. (2024) — 8–10% of algorithmic recommendations are bad, but…: https://doi.org/10.1016/j.ijinfomgt.2023.102743
Define measurable risk–utility trade-offs instead of assuming all recommendations are harmful.

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 ranking rules reduce repeated exposure to risky proxy content without destroying useful relevance and diversity?

EXAMPLE TOOLS
Python, pandas, NumPy, scikit-learn, FastAPI, Streamlit or React
Equivalent tools are fine.

FIRST THREE ACTIONS
1. Write the schema and generator distributions; independently review 30 items and 10 profiles before generating the fixed benchmark.
2. Implement chronological and engagement-only ranking first; add deterministic replay and invariant tests.
3. finalise measures, seeds and policy parameters before running the remaining rankers and reviewer study.

LITERATURE SEARCH
recommender system algorithmic amplification extremism audit exposure diversity risk utility

DATA
1,200-item controlled feed benchmark (planned)
Create 1,200 synthetic, non-extremist items across 12 proxy topics and 60 sources. Store item_id, source_id, topic, created_at, relevance, predicted_engagement, quality, risk_proxy and annotator confidence. Create 200 fixed user profiles with topic preferences and exposure history. No live platform accounts or scraped extremist content.


HOW TO TEST IT
Run every ranker on the same 200 profiles and 20 sessions with fixed seeds. Report risky-exposure@10, source/topic concentration, intra-list diversity, NDCG@10 for benign relevance, calibration and latency. Compare policies with paired profile-level intervals. In a task study, test whether the audit view helps 8 reviewers identify the rule responsible for a harmful exposure pattern.

FINAL RESULT
Deterministic feed simulator, five rankers, audit dashboard, benchmark and policy trade-off report.

ACCESS OR PEOPLE
Independent review of the synthetic labels and scenarios; recruit 8 reviewers for the master task study.

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