Analysis: "@fay @aberdeedshire How the Green movement kills: 31,000 ex...

12y ago Nov 30, 2013 at 7:46 PM

"@fay @aberdeedshire How the Green movement kills: 31,000 excess winter deaths in Britain last year. Each turbine kills 6 pensioners."

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AI Analysis

Machine-generated analysis of the post above on 2026-02-27. Not written by the author of the post.

Danger Level

Elevated

Narcissistic State

Grandiose

Authorship

Uncertain

Intensity

65%

This tweet is clinically notable as a study in rationalization and concealed self-interest. Trump's sustained anti-wind Twitter campaign on Black Friday 2013 is inseparable from his active legal battle against Scottish authorities over the Aberdeen Bay Wind Farm threatening his Aberdeenshire golf resort — context that reframes his humanitarian concern for dying pensioners as motivated reasoning. The rhetorical operation is sophisticated: a genuine ONS statistic (31,280 excess winter deaths, 2012/13, published just four days earlier on November 26) is extracted from its multi-causal context — where respiratory disease (37%), circulatory disease (26%), and dementia dominate the etiology — and re-attributed entirely to "the Green movement," then divided by UK turbine count to produce the pseudo-empirical "6 pensioners per turbine" figure. False precision deployed for shock and virality. Defense mechanisms present include rationalization (conclusion-first statistical deployment), projection (attributing lethal consequences to financial opponents), and splitting (binary killer/protector framing). The warrior/protector archetype is operative: Trump as truth-teller exposing establishment body counts, with pensioners selected as maximally sympathetic victims. A probable typo (@aberdeedshire for @aberdeenshire) is consistent with authentic authorship despite mid-afternoon posting. Cognitive function appears consistent with 2013 baseline. Danger is elevated — attributing thousands of deaths to a named political movement normalizes hostility toward climate advocates — but no individual targeting or mobilization is present.

Authorship Analysis

Uncertain

Indicators:

Mid-afternoon timing (14:46 EST) is consistent with business hours, moderately suggesting aide involvement
Typo '@aberdeedshire' for '@aberdeenshire' is characteristic of unpolished authentic Trump posts; a vetted aide would have verified the handle
Sustained same-day anti-wind tweet cluster (at least 5 posts) suggests emotionally engaged personally motivated session rather than aide curation
Trump had acute personal financial stake in defeating Aberdeenshire wind farm — active legal challenge filed May 2013 was ongoing at time of posting
Punchy false-precision formulation ('kills 6 pensioners') matches Trump's documented rhetorical fingerprint

Psychological Profile

▶ State

Grandiose State

Trigger: Maintenance (Ongoing personal legal battle against Aberdeenshire offshore wind farm (Aberdeen Bay Wind Farm) threatening Trump International Golf Links Scotland; sustained anti-wind public relations campaign coinciding with fresh ONS statistics entering his Twitter ecosystem)

Rage: Intensity 50% targeting The Green movement / renewable energy advocates / Scottish government authorities supporting offshore wind development

Proportionality

20%

Sentiment

-0.75

Mildly Hypomanic

At least 5 anti-wind energy posts identified on this single day, suggesting sustained goal-directed thematic focusRapid engagement with multiple followers (@fay, @aberdeedshire, @TundraSwans, @WalterHorsting, @noturbine, @Al Co) on a single topicAfternoon tweet cluster intensity consistent with elevated engagement around a personally salient topic

▶ Clinical

Malignant Narcissism:

Narcissistic

55%

Antisocial

50%

Paranoid

45%

Sadism

15%

Defense Mechanisms:

rationalizationprojectionsplitting

Cognitive Complexity:

Complexity

25%

Parasocial Techniques:

Direct reply to named followers (@fay, @aberdeedshire) creates parasocial intimacy and sense of personal engagementPositioning as insider truth-teller with access to suppressed or under-reported information ('How the Green movement kills')Appeals to shared moral values (protection of pensioners, opposition to callous green establishment) to bond with audienceOutrage-sharing format invites followers to join morally righteous collective against a named enemy

Danger Assessment

Elevated

Indicators:

Death attribution to named political movement ('the Green movement kills') normalizes framing of political opponents as murderers, contributing to a rhetorical climate of hostility toward climate advocates
False precision in casualty count ('6 pensioners per turbine') lends spurious empirical credibility to an inflammatory claim, increasing psychological persuasiveness and potential for real-world hostility
Sustained same-day campaign (5+ anti-wind posts) suggests deliberate audience mobilization rather than impulsive individual post
Framing environmental policy as equivalent to killing vulnerable elderly people provides moral license for aggressive counter-action

Gaslighting Detected:

Misattributes multiply-determined winter deaths (respiratory disease 37%, circulatory 26%, cold housing) exclusively to green energy policy
Per-turbine death calculation presents spurious arithmetic as researched empirical finding
Frames personally financially motivated opposition to wind energy as disinterested humanitarian concern for pensioners
Fresh ONS statistics are stripped of their actual causal framing and re-attributed to a predetermined ideological target

Reality Distortions:

Attribution of 31,280 excess winter deaths exclusively to 'the Green movement,' ignoring the primary causes identified by ONS (respiratory disease 37%, circulatory disease 26%, dementia, cold housing stock, influenza)
Presentation of a spurious arithmetic operation (deaths ÷ turbines) as an established empirical finding about causation
Framing of personally financially motivated anti-wind opposition as disinterested principled concern for dying pensioners

Rhetorical Analysis

Dehumanizing Language Present

Violent Imagery Present

Fact Checks (3)

"31,000 excess winter deaths in Britain last year"

Half True

ONS reported 31,280 excess winter deaths in England and Wales for 2012/13, published November 26, 2013 — four days before this tweet. The figure is approximately accurate. However, 'Britain' vs. 'England and Wales' is imprecise (Scotland excluded from ONS reporting). Critically, the causal attribution to 'the Green movement' is fabricated: ONS data identifies respiratory diseases (37%), circulatory diseases (26%), and dementia as the primary drivers. Fuel poverty contributes to an estimated 10-21% of such deaths; green energy levies are one contested contributor to fuel bills among many factors.

"Each turbine kills 6 pensioners"

False

This figure appears arithmetically derived by dividing ~31,000 deaths by the approximate UK installed wind turbine count (~5,000 in late 2013), yielding ~6.2. The arithmetic is reproducible; the causal logic is entirely spurious. ONS data identifies respiratory disease, cardiovascular disease, and cold housing — not wind turbines — as the proximate causes of excess winter deaths. There is no established epidemiological mechanism linking wind turbine presence to pensioner mortality. This is a rhetorical device masquerading as empirical finding.

"The Green movement kills (as a general causal claim for winter deaths)"

Mostly False

No credible epidemiological literature supports attributing excess winter deaths to the 'Green movement' as such. While green energy levies have contributed to UK energy price increases and thus potentially to fuel poverty, this is one partial and contested pathway among many. BBC research in 2014 found excess winter deaths were not straightforwardly correlated with fuel poverty rates by geography. The framing attributes a multi-determined public health phenomenon to a single ideological movement.

Receipts

No contradictions with other posts detected yet.

Authorship Attribution

UTC 19:46:47 converts to 14:46 EST (Eastern Standard Time, UTC−5) on November 30, 2013 — Black Friday weekend. This places the tweet in business hours, the primary indicator of aide-assisted posting. However, several factors push toward authentic Trump: (1) Typo: @aberdeedshire is a misspelling of @aberdeenshire — a vetted aide managing official communications would almost certainly have verified the handle before posting; (2) Personal financial stake: Trump was at this exact moment engaged in an active legal challenge (filed May 2013) against the Scottish government over the Aberdeen Bay Wind Farm project adjacent to his Trump International Golf Links Scotland — making this topic acutely personal rather than abstractly political; (3) Sustained same-day cluster: All adjacent tweets form a thematically unified anti-wind energy campaign, consistent with emotionally invested direct posting rather than aide curation; (4) Fresh statistics: The ONS "Excess Winter Mortality in England and Wales: 2012 to 2013" report was released on November 26, 2013 — just four days before this tweet — explaining how the 31,000 figure had just entered the anti-wind Twitter ecosystem Trump was actively monitoring.

The compressed, punchy voice — with the false-precision formulation "Each turbine kills 6 pensioners" — is characteristic of Trump's rhetorical fingerprint. Assessment: medium confidence, leaning authentic.

Fact Verification

Claim	Verdict	Evidence
"31,000 excess winter deaths in Britain last year"	Half True	ONS reported 31,280 excess winter deaths in England and Wales for 2012/13, published November 26, 2013 — four days before this tweet. The figure is approximately accurate. However, 'Britain' vs. 'England and Wales' is imprecise (Scotland excluded from ONS reporting). Critically, the causal attribution to 'the Green movement' is fabricated: ONS data identifies respiratory diseases (37%), circulatory diseases (26%), and dementia as the primary drivers. Fuel poverty contributes to an estimated 10-21% of such deaths; green energy levies are one contested contributor to fuel bills among many factors.
"Each turbine kills 6 pensioners"	False	This figure appears arithmetically derived by dividing ~31,000 deaths by the approximate UK installed wind turbine count (~5,000 in late 2013), yielding ~6.2. The arithmetic is reproducible; the causal logic is entirely spurious. ONS data identifies respiratory disease, cardiovascular disease, and cold housing — not wind turbines — as the proximate causes of excess winter deaths. There is no established epidemiological mechanism linking wind turbine presence to pensioner mortality. This is a rhetorical device masquerading as empirical finding.
"The Green movement kills (as a general causal claim for winter deaths)"	Mostly False	No credible epidemiological literature supports attributing excess winter deaths to the 'Green movement' as such. While green energy levies have contributed to UK energy price increases and thus potentially to fuel poverty, this is one partial and contested pathway among many. BBC research in 2014 found excess winter deaths were not straightforwardly correlated with fuel poverty rates by geography. The framing attributes a multi-determined public health phenomenon to a single ideological movement.

Overall Veracity: 23%

Psychological Analysis

This post cannot be read without its biographical substrate. By November 2013, Trump had been engaged for over two years in a publicly documented, personally consuming legal and lobbying campaign to block the Aberdeen Bay Wind Farm — an 11-turbine offshore array approximately 3.5 km from his Aberdeenshire golf resort. He had met personally with First Minister Alex Salmond, employed legal and PR teams, and made the conflict highly personal. His framing here as humanitarian defender of dying pensioners conceals this financial self-interest beneath a moral veneer of striking rhetorical effectiveness.

Dominant Defense — Rationalization (neurotic): Trump deploys a genuine statistic (fresh ONS winter deaths data) as the substrate for a false causal narrative. The mechanics follow a classic motivated-reasoning pattern: the conclusion (wind energy is bad) precedes the evidence-gathering, which then works backward to construct a justificatory framework. The 31,000 deaths number — real, alarming, and freshly published — is laundered through false attribution to produce moral outrage directed at personal opponents.

Secondary Defense — Projection (immature): By attributing lethal consequences to the Green movement, Trump deflects scrutiny from his own financially motivated opposition to wind energy. The implicit moral frame positions him as a life-protector, not a property-interest defender.

Tertiary Defense — Splitting (immature): "How the Green movement kills" constructs an absolute moral binary. No acknowledgment of multi-causation, no gradient, no complexity. The green movement is monolithically lethal; its opponents are implicitly life-sustaining.

Warrior/Protector Archetype: Trump casts himself as truth-teller exposing the establishment's concealed body count. The selection of "pensioners" as victims is rhetorically maximizing — the elderly carry the highest sympathetic moral weight in public discourse, and their deaths constitute the most powerful possible indictment of an opponent. The archetypal structure positions Trump as the warrior who speaks forbidden truths on behalf of those who cannot defend themselves.

Grandiose narcissistic state: Trump positions himself as the revealer of suppressed truths — claiming epistemic access that mainstream media and green advocates conceal — implicitly asserting superior moral clarity and factual command over the situation.

Rhetorical Analysis

In 140 characters, this tweet delivers a complete propaganda structure: perpetrator (Green movement) + victims (pensioners) + mechanism (turbines) + body count (6 each). The colon after "How the Green movement kills:" performs the epistemic function of a verdict — it signals that what follows is evidence, not opinion. The verb "kills" — rather than softer alternatives ("contributes to," "is associated with," "may increase risk of") — forecloses ambiguity and frames policy advocates as functionally equivalent to murderers.

The false precision of "6 pensioners per turbine" is the tweet's most sophisticated manipulation. The psychological literature on the precision heuristic establishes that specific numbers are intuitively perceived as researched and credible — a round number would invite skepticism; "6" sounds measured. The calculation exists; only the causal logic is absent. This lends the post an air of analytical rigor it entirely lacks.

The @aberdeedshire tag (misspelling of @aberdeenshire) may represent Trump attempting to tag Aberdeenshire Council — his direct legal adversary in the wind farm case — while simultaneously performing for his Twitter audience. If so, it is a directed public confrontation masquerading as information-sharing.

Cognitive Status

The tweet is too brief for meaningful formal assessment. The handle misspelling is consistent with Trump's documented 2013 baseline typo patterns. The logical chain — while causally false — is internally coherent and arithmetically grounded. No language production anomalies detected. Consistent with 2013 baseline.

Danger Assessment

The framing of a named political movement as responsible for thousands of deaths ("the Green movement kills") elevates rhetorical temperature beyond policy disagreement, normalizing the characterization of environmental advocates as murderers. The false precision of the "6 pensioners" figure amplifies this by lending the accusation a pseudo-empirical credibility that purely opinion-based attacks lack. While this tweet does not identify individual targets, call for action, or use eliminationist vocabulary targeting persons, it contributes to a rhetorical environment in which hostility toward climate activists is morally framed as self-defense against killers. Danger level: elevated.