Newphoria tracks how 16 news sources cover the same stories. Here's the raw data — no editorial judgment, just observable patterns.
Every article is classified into one of four Bloom categories based on its tone, framing, and constructiveness:
GOOD stories highlight solutions, progress, or constructive framing. BAD stories focus on problems without resolution. UGLY stories use inflammatory language designed to provoke outrage. WEIRD stories are genuinely unusual or surprising.
Bloom classification is performed by AI analysis, not by human editors making subjective judgments. The system examines article text for linguistic markers, framing patterns, and tonal indicators.
This is automated pattern recognition, not editorial opinion. Two reasonable people might classify the same article differently — the AI applies consistent rules across all sources equally.
When we say a source "over-indexes" or "under-indexes" in a category, we mean it deviates from the statistical average across all 16 sources. This is a mathematical observation, not a political judgment.
A war correspondent will naturally score more BAD than a science reporter. That reflects their beat assignment, not editorial bias. Context matters — these numbers describe coverage patterns, not quality.
Source Transparency is one layer of Newphoria's analytical stack. For richer context, explore these companion tools:
Events Intelligence →Track how different sources cover the same events in real time, revealing editorial priorities and framing differences.
Divergence Report →See where source coverage diverges most from the consensus narrative, highlighting unique angles and blind spots.