# 7. Algorithmic Perception Control

> Defensive, educational synthesis. Operational influence guidance is intentionally excluded.

**Primary level:** Environment

**Evidence maturity:** Documented current effects

Ranking, recommendation, search, trending, moderation, and notification systems shape what people notice, regard as important, believe is popular, or treat as credible.

## Evidence boundary

Ranking and visibility effects are established, but claims that algorithms uniformly radicalize or determine political beliefs are highly context dependent and contested.

## Why it matters

- People infer credibility and social consensus from rank, repetition, and visible engagement.
- External actors can exploit the same systems that platforms optimize internally for attention, creating overlapping responsibility.

## Defensive focus

- Give users meaningful feed and ranking controls.
- Disclose major ranking objectives and policy changes.
- Audit exposure rather than only engagement.
- Provide vetted researcher access with privacy safeguards.
- Use human review for coordinated-reporting enforcement.
- Add friction and diversity constraints where evidence supports them.

## Research gaps

- Causal effects across different platform architectures.
- Auditing individualized feeds without privacy intrusion.
- Impact of synthetic engagement on ranking models.
- How visibility interventions affect marginalized and small communities.

## Selected sources inherited from the supplied report

- [How do social media feed algorithms affect attitudes and behavior in an election campaign?](https://cdr.lib.unc.edu/downloads/wp9891401) — report reference 2
- [Understanding Social Media Recommendation Algorithms | Knight First Amendment Institute](https://knightcolumbia.org/content/understanding-social-media-recommendation-algorithms) — report reference 6
- [Search engine manipulation effect - Wikipedia](https://en.wikipedia.org/wiki/Search_engine_manipulation_effect) — report reference 7
- [Ephemeral Astroturfing Attacks: The Case of Fake Twitter Trends - Rebekah Overdorf](https://overdorf.github.io/assets/papers/21EuroSP.pdf) — report reference 14
- [STRIDE Threat Model - Simplified - Real Attack Examples (2026) - Practical DevSecOps](https://www.practical-devsecops.com/what-is-stride-threat-model/) — report reference 21
- [Interactions-HSG/SOAP: SOAP - A Sockpuppet Auditing Tool for Very Large Online Platforms - GitHub](https://github.com/Interactions-HSG/SOAP) — report reference 38
- [Auditing Meta and TikTok Research API Data Access under Article 40(12) of the Digital Services Act - arXiv](https://arxiv.org/html/2601.12390v1) — report reference 44
- [Auditing Meta and TikTok Research API Data Access under Article 40(12) of the Digital Services Act - ResearchGate](https://www.researchgate.net/publication/399930369_Auditing_Meta_and_TikTok_Research_API_Data_Access_under_Article_4012_of_the_Digital_Services_Act) — report reference 45
- [How Social Media Algorithms Work in 2026: Full Guide - Digital Applied](https://www.digitalapplied.com/blog/how-social-media-algorithms-work-2026) — report reference 23

Primary report SHA-256: `050bbe17757676e81641a6fe6d31b5959aa2b4966289f4020d632cd6d181776a`

External links and current claims were not independently reverified in this release.