Under The Hood X Report Analyzer
Safety & Visibility Guide

How Reports Affect Post Visibility

Reports are different from likes, replies, reposts and other ordinary engagement. The current Phoenix model explicitly predicts report as a negative action. The ranking system also includes a corresponding negative weight. (Phoenix runner)

But reports also connect to visibility and safety systems, making this signal particularly important.

Report ≠ Automatic Removal

A report should not be described as: “One report automatically removes a post.” The public recommendation architecture does not establish such a rule.

A report is a signal that can indicate a potentially problematic recommendation, while separate moderation and visibility systems determine whether content should remain available or be restricted. The older X recommendation repository documents visibility filtering as a separate component responsible for hard filtering, visible treatments and coarse-grained downranking. (X original recommendation algorithm)

Recommendation Scoring vs. Visibility

These are two different concepts:

Recommendation Should this candidate be recommended to this user?
Visibility Should this content be available or treated differently at all?

A report can be relevant to the first through negative prediction and to broader safety/visibility systems through separate mechanisms.

Report
├── Recommendation signal
└── Safety / visibility systems → Possible action

The exact action depends on the platform's applicable systems and policies.

Why Reports Are Different from Ordinary Negative Feedback

A user saying Not Interested generally expresses a preference. A report can indicate that the user believes content may violate rules or otherwise requires review. That's why you should avoid putting reports into the same bucket as “This person didn't like my post.” The platform can use reporting within systems that are separate from ordinary recommendation ranking.

Under The Hood Takeaway

A report is both a negative recommendation signal and potentially relevant to separate visibility/safety systems. It does not establish an automatic post-removal rule.

How X Ranks and Recommends Posts

How “Not Interested” Affects Recommendations How Reports Affect Post Visibility How Blocks and Mutes Affect Post Recommendations How Clicks Affect X Reach How Quotes Affect X Reach How Reposts Affect X Reach How Replies Affect X Reach How Likes Affect X Reach Positive vs Negative Signals on X Which Engagements Matter Most on X? How X Predicts Engagement How X Scores Your Posts How X Decides What to Recommend

Official Open-Source References