By Cliff Potts, CSO, and Editor-in-Chief of WPS News

Baybay City, Leyte, Philippines — March 5, 2026

Purpose

This framework provides a simple, neutral, and replicable method for documenting whether posts published on LinkedIn are delivered to followers as users are led to expect.
It is designed to turn individual experiences into comparable evidence that can be evaluated by journalists, researchers, and regulators.

This document does not assume intent or illegality. It standardizes observation.

What Counts as Evidence

To be usable, a submission must include timestamps and confirmation from at least one known follower. Opinion, speculation, or screenshots without timing context do not qualify.

Required Data (All Submissions)

Please record each item below for every test:

  1. Post Date & Time (Local Time + Time Zone)
    Example: March 4, 2026 — 9:12 a.m. ET
  2. Posting Account
    Public profile URL (no private data)
  3. Post Type
    Text / Link / Image / Video
  4. Confirmed Followers Checked
    Number of followers who agreed to check their feed (minimum: 1)
  5. Delivery Result
    • Appeared in feed: Yes / No
    • Time checked after posting (e.g., 15 min, 1 hr, 24 hrs)

Optional Supporting Material (Recommended)

  • Screenshots of follower feeds showing absence or presence
  • Follow confirmation (e.g., follower confirms they follow the account)
  • Repeat tests of the same account over multiple days

Optional material strengthens submissions but is not required.

How to Run a Basic Test

  1. Publish a standard post on LinkedIn (no rule violations).
  2. Immediately notify one or more known followers to watch their feed.
  3. Have followers check their feed at two intervals (e.g., 15–30 minutes and 24 hours).
  4. Record whether the post appears without searching the poster’s profile directly.
  5. Log results using the required data fields above.

Consistency matters more than volume.

What Does Not Count

  • Searching for a post on the author’s profile
  • Algorithm speculation or engagement guesses
  • Claims without timestamps
  • Anonymous anecdotes without verification
  • Reactions to low engagement metrics alone

This framework measures delivery, not popularity.

Submission Guidelines

  • Keep language factual and neutral
  • Do not infer motive or intent
  • One post = one entry
  • Multiple entries from the same account are encouraged if run on different days

Aggregated patterns, not individual outcomes, are the objective.

Why This Framework Exists

LinkedIn is widely treated as professional infrastructure. If following does not reliably result in content delivery, users may be making labor and career decisions based on inaccurate assumptions.

Standardized documentation allows third parties to determine:

  • whether non-delivery is systematic
  • whether it varies by account type or content
  • whether marketing claims align with observed behavior

Closing Note

This framework is intentionally modest. It does not accuse. It records.

If the system works as marketed, the data will show it.
If it does not, the record will exist.

That transparency benefits everyone.

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