The tone of mainstream media coverage of Parliament, tracked from a fixed allowlist of Kenyan outlets — see exactly how it works.
What the National Assembly is working on — for each Bill: its current
stage, how media is covering it, and what
citizens have submitted. These are three separate readings,
never blended into one score.
Parliamentary activity as of the last recorded
order paper (sitting): 2 Jul 2026 — this can lag by a
few weeks, and Parliament may be in recess.
Everything below measures the tone of media coverage of Parliament across all topics — background context for the Bills above. It is not public opinion and not a poll.
Topics ranked by current coverage volume × how far their tone moved against the trailing four-bucket baseline (weekly buckets where the week has 5+ reviewed items, else monthly). A topic only ranks when both windows clear 5 items.
Monthly sample sizes: 2026-06 (n = 6) · 2026-07 (n = 8). Months with fewer than 5 reviewed items are not plotted.
A single item can mention several topics, so these mentions sum to more than the 14 items analysed. Topics under 5 mentions show no average.
| Topic | Mentions (n) | Average tone |
|---|---|---|
| Governance & Constitution | 21 | -0.06 (n = 21) |
| Devolution | 20 | -0.29 (n = 20) |
| Education | 18 | -0.24 (n = 18) |
| Security | 15 | -0.64 (n = 15) |
| Economy & Taxation | 14 | -0.29 (n = 14) |
| Healthcare | 8 | -0.29 (n = 8) |
| Corruption & Accountability | 7 | -0.38 (n = 7) |
| Agriculture | 1 | too few (n = 1) |
| Source | Items (n) | Average tone |
|---|---|---|
| The Standard | 50 | -0.30 (n = 50) |
| Nation | 5 | -0.47 (n = 5) |
| The Star | 1 | too few (n = 1) |
Every figure on this page is built from items like these — each linked to its source.
This page tracks the tone of media coverage — it is NOT a statistical opinion poll and never measures public opinion or approval. The pipeline: articles from a fixed allowlist of Kenyan outlets (Nation, The Standard, The Star, People Daily, Citizen Digital, NTV, K24, KTN News, KBC and Business Daily) are ingested every few hours; a classifier first scores each item’s relevance to the Parliament of Kenya, and items below the relevance floor are rejected outright.
Surviving items are classified positive / neutral / negative (with a −1.0 to +1.0 score) by an AI language model; confident calls publish, borderline ones wait in a human review queue. Items are then linked to the members and Bills they name, with uncertain links held for editor review. No average is shown from fewer than 5 reviewed items, every average is printed with its sample size, and every figure traces to the listed items with source, link and date. Full details: methodology.