heatmap.news Media Bias



Identity: The evidence strongly indicates this is Heatmap (heatmap.news), a climate/energy newsletter: articles reference 'Heatmap correspondent Matthew Zeitlin'  

, 'Heatmap House' events     , and 'Heatmap Pro' proprietary data     .

Topic coverage: Overwhelmingly US energy and climate policy: permitting reform (BAAJA appears in at least seven pieces  

            ), data centers       , nuclear/SMRs     , solar     , geothermal     , insurance markets   , and oil/gas dispatches     .

Keyword tracking confirms 'diesel export ban, hurricane isaias, nuclear power, electric vehicles' [26].

General framing: A left-of-center, pro-decarbonization, pro-climate-action outlet.

The Trump administration's energy agenda is framed in conflict-laden terms: 'war on wind'  

, CAFE rollback 'render[s] the fuel economy law… moot'   , and 'neutering' of standards   .

AI energy use is framed as a climate threat   .

No counterexample appears in the sample where Trump policy is framed positively, though the outlet does frame some deregulatory items neutrally ('industry-requested relief')   and treats oil/gas expansion as pragmatic     — partial counterweights.

Coverage patterns: Heavy use of insider political framing — senators, union positions, election timing  

      — and a recurring 'winners and losers' market-opportunity lens on climate tech       .

Notably, data-center and growth topics receive a largely pro-development, pro-market treatment: dividend proposals go unchallenged   , data-center demand is treated as durable     , and nuclear/Big Tech deals are framed as pragmatic hedges   . This gives it a technocratic-pragmatist, not purely activist, character.

Observable propaganda/techniques: Evidence of loaded language ('war on wind'  

, 'legal brawl is brewing'   , 'begging'   , 'gas guzzlers… Priuses'   ) and editorializing inside news digests ('At last, right after hopes dimmed, we have a deal'   ; 'the current system sucks'   ).

Mitigating counterexamples: consistent attribution, corrections, and disclosed uncertainty ('likely an undercount'   , 'estimates are certainly on the high end'   , disclosure of prior positions   , editor's note corrections   ) indicate above-average integrity and methodological self-awareness.

AI-authorship assessment: The conversational first-person voice, personal asides (the BAAJA/'Basha' grandmother joke  

), humor ('forgive me — hot'   ), and transparent self-disclosure   are more consistent with human-authored opinion journalism than AI generation; automated-digest style in       is the only piece resembling templated output, and that is probabilistic, not conclusive.

Highest 3 values:  

Decarbonization/clean energy     ;   evidence transparency/intellectual honesty       ;   pragmatic infrastructure/building — permitting reform, market growth       .

Lowest 3 values:  

political neutrality — overt anti-Trump framing       ;   neutrality on fossil fuels — mining and oil framing presupposes environmental risk     ;   detachment from political actors — soft interviews and event coverage of allied figures (Gov.

Sherrill) with questions that 'invite elaboration rather than critical testing'     .

Reliability: Factual-reliability evidence is comparatively strong: named sourcing, peer-reviewed citations  

, legal detail     , and hedged projections     .

Bias is chiefly in selection and framing, not fabrication.



Helium Bias: I inferred outlet identity from internal references     , not explicit confirmation.

The 27-article sample spans ~Sept–Oct 2026, is small, and may over-represent the permitting-bill cycle; I cannot see stories the source declined to publish.

Bias annotations were supplied by a third-party annotator whose methodology I cannot verify.

AI-authorship and propaganda judgments are probabilistic inferences from writing style, not established facts.

Automated source summary · Updated October 12, 2026 · Not human reviewed. Check recent article panels for claim-level evidence when available.




Use the Data in AI All Sources

heatmap.news Bias Profile

Weighted source-level patterns from recent analyzed coverage. Open recent articles below to inspect score-specific evidence and limitations when available.

🔵 Liberal <—> Conservative 🔴-9

📉 Bearish <—> Bullish 📈6

💡 Boring <—> Interesting20

😨 Fearful10

📞 Begging the Question6

💭 Opinion75

🗳 Political38

Oversimplification12

🏛️ Appeal to Authority22

👀 Covering Responses13

😢 Victimization10

😤 Overconfidence20

🔒 Ideological20

❌ Low Credibility <—> High Credibility ✅27

🧠 Rational <—> Irrational 🤪-14

🤑 Advertising8

🤖 Written by AI6

💔 Low Integrity <—> High Integrity ❤️18

🪨 Low Intelligence <—> High Intelligence 🦉48

🎭 Virtue Signaling18

🔍 Truth-seeking <—> Delusion 🌀-12

🔬 Scientific <—> Superstitious 🔮-10

🎲 Speculation27

🐍 Manipulative27

Subtle dimensions

🧢 Populist <—> Elitist 🎩-3

🗽 Libertarian <—> Authoritarian 🚔2

🗞️ Objective <—> Subjective 👁️ 0

🚨 Sensational0

😩 Pessimistic <—> Optimistic 🌞4

📝 Prescriptive0

🕊️ Dovish <—> Hawkish 🦁1

🗣️ Gossip0

🍼 Immature2

🔄 Circular Reasoning0

🗑️ Spam2

🏴 Anti-establishment <—> Pro-establishment 📺4

📏📏 Double Standard4

✊ Woke5

🔺 Conspiracy5

🐐 Scapegoating2

🤡 Hypocrisy0

🦊 Anti-Corporate <—> Pro-Corporate 👔3

👤 Individualist <—> Collectivist 👥1

How to interpret source scores →

Average social shares per article 0






Click points to explore news by date. News sentiment ranges from -10 (very negative) to +10 (very positive) where 0 is neutral.





heatmap.news Recent Articles



Sort By: