Josh Weil - Florida Progressive Democrat US Inactive
JOSH WEIL FOR CONGRESS
Flip this seat >>
Ad copy
I'm running against Trump and DeSantis' hand-picked nominee, Randy Fine. He's an ultra-MAGA extremist who will be a rubber stamp on everything they want to do in Congress. To stop him, we need every Democrat in the country to pitch in and help. I'm Josh Weil, a public educator and Democratic nominee for the April 1st special election in Florida's 6th Congressional District. Please donate today, and with your support we can flip this seat, tie up Congress and take the wind out of Trump's MAGA movement. Josh Weil Democrat for U.S. Congress (FL-6)
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Performance proxies
| Overall score | 51.3/100 |
| Spend / Reach / Long. / Eff. | 59.4 · 87.0 · 2.5 · 56.4 |
| Spend range | USD 10,000–14,999 |
| Impressions | 250,000 – 299,999 |
| CPM (≈ $/1k impr) | USD 45.45 |
| Est. audience size | — |
| Days live | 10 (2025-03-12 → 2025-03-22) |
| Created | 2025-03-12 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | FL — Joshua Weil |
| Surfaced by | “senate:Joshua Weil” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | urgency |
| Production tier | diy amateur |
| Who pictured | candidate |
| Symbols | none |
| Call to action | donate |
| Standout element | Casual selfie-style video intro with sunglasses and conversational overlay text creating an authentic grassroots feel |
| Tactic | Uses urgent contrast framing — casting opponent as a Trump/DeSantis rubber stamp — to motivate out-of-district Democratic donors to fund a longshot special election flip. |
Where it ran (delivery by region)
| Florida | 16.2% |
| California | 12.8% |
| New York | 6.7% |
| Washington | 4.4% |
| Massachusetts | 4.0% |
| Texas | 3.5% |
| Illinois | 3.4% |
Who saw it (delivery)
| 18-24 · female | 0.4% |
| 45-54 · male | 5.1% |
| 65+ · male | 12.2% |
| 65+ · female | 29.9% |
| 55-64 · unknown | 0.3% |
| 55-64 · male | 8.3% |
| 55-64 · female | 16.9% |
| 45-54 · unknown | 0.2% |
| 45-54 · female | 10.3% |
| 18-24 · male | 0.4% |
| 35-44 · unknown | 0.2% |
| 35-44 · male | 4.1% |
| 35-44 · female | 7.0% |
| 25-34 · unknown | 0.1% |
| 25-34 · male | 1.9% |
| 25-34 · female | 2.2% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.5% |
