Cooper for North Carolina
Rush a donation to help turn North Carolina blue!
We officially have our Trump-picked Republican opponent. RNC Chair Michael Whatley is Donald Trump’s TOP recruit in the MOST competitive U.S. Senate race of 2026. Roy Cooper will stand strong for our communities and fight for families here in North Carolina and beyond. Michael Whatley is a yes-man who would stand only for Donald Trump. We don’t need another far-right rubber stamp in Washington. We NEED Roy fighting for our families — and that means we need you to step up. Pitch in ASAP to help Roy Cooper defeat Donald Trump’s handpicked candidate in NC 👇
↗ View in Meta Ad Library (the live creative — image/video/layout)
| Overall score | 48.5/100 |
| Spend / Reach / Long. / Eff. | 50.6 · 81.7 · 3.0 · 58.6 |
| Spend range | USD 5,000–5,999 |
| Impressions | 150,000 – 174,999 |
| CPM (≈ $/1k impr) | USD 33.84 |
| Est. audience size | — |
| Days live | 12 (2025-09-08 → 2025-09-20) |
| Created | 2025-09-08 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | NC — Roy Cooper |
| Surfaced by | “senate:Roy Cooper” |
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | urgency |
| Production tier | semi pro |
| Who pictured | candidate, opponent |
| Symbols | none |
| Call to action | donate |
| Standout element | Warning triangle with 'TOSS UP' framing the race as a competitive emergency requiring immediate financial action |
| Tactic | The ad uses a race-urgency frame — pairing 'TOSS UP' with a warning icon and a direct 'DONATE TO DEFEAT' CTA — to convert competitive-race anxiety into immediate donor action against a Trump-branded opponent. |
| North Carolina | 48.4% |
| California | 4.8% |
| Virginia | 4.7% |
| New York | 3.5% |
| Florida | 3.2% |
| South Carolina | 3.0% |
| Massachusetts | 2.6% |
| 18-24 · female | 0.1% |
| 45-54 · male | 4.7% |
| 65+ · male | 25.4% |
| 65+ · female | 31.3% |
| 55-64 · unknown | 0.3% |
| 55-64 · male | 9.6% |
| 55-64 · female | 11.9% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 6.7% |
| 18-24 · male | 0.1% |
| 35-44 · unknown | 0.1% |
| 35-44 · male | 2.9% |
| 35-44 · female | 3.8% |
| 25-34 · unknown | 0.0% |
| 25-34 · male | 1.0% |
| 25-34 · female | 1.2% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.8% |