GRAHAM FOR MAINE
If you’re ready to defeat Susan Collins, then chip in right now to Graham Platner's campaign.
It’s Elizabeth Warren. I just endorsed Graham Platner in Maine’s important U.S. Senate race — and I know he can win and defeat Susan Collins this November. Let me tell you why. Graham is a fighter for working people. In the Senate, he’ll stand up to Donald Trump and his billionaire buddies, push for big, structural change, and be the leader that Maine needs. Democrats need to flip just four seats to take back our majority in the Senate, and this election in Maine will be consequential. If you’re ready to flip this seat and defeat Susan Collins, please chip in to support Graham Platner’s campaign today.
↗ View in Meta Ad Library (the live creative — image/video/layout)
| Overall score | 60.0/100 |
| Spend / Reach / Long. / Eff. | 77.1 · 100.0 · 9.1 · 53.7 |
| Spend range | USD 60,000–69,999 |
| Impressions | 1,000,000 – 0 |
| CPM (≈ $/1k impr) | USD 65.0 |
| Est. audience size | — |
| Days live | 34 (2026-03-19 → 2026-04-22) |
| Created | 2026-03-19 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | ME — Graham Platner |
| Surfaced by | “senate:Graham Platner” |
| Issue | jobs labor |
| Framing | positive candidate |
| Message type | fundraising |
| Emotional appeal | hope |
| Production tier | diy amateur |
| Who pictured | candidate, workers labor |
| Symbols | none |
| Call to action | donate |
| Standout element | Elizabeth Warren in signature bright green blazer delivering a personal endorsement video alongside the candidate in a casual home setting |
| Tactic | The ad leverages Elizabeth Warren's high-profile endorsement as borrowed credibility to energize the progressive base and drive small-dollar donations against incumbent Susan Collins. |
| California | 12.1% |
| Maine | 11.5% |
| Massachusetts | 8.6% |
| New York | 7.8% |
| Florida | 5.7% |
| Texas | 3.5% |
| Washington | 3.4% |
| 18-24 · female | 0.7% |
| 45-54 · unknown | 0.2% |
| Unknown · male | 0.0% |
| Unknown · female | 0.0% |
| 65+ · unknown | 0.6% |
| 65+ · male | 15.7% |
| 65+ · female | 29.4% |
| 55-64 · unknown | 0.3% |
| 55-64 · male | 8.2% |
| 55-64 · female | 11.8% |
| 45-54 · male | 5.7% |
| 18-24 · male | 1.9% |
| 45-54 · female | 5.8% |
| 35-44 · unknown | 0.2% |
| 35-44 · male | 6.5% |
| 35-44 · female | 4.7% |
| 25-34 · unknown | 0.2% |
| 25-34 · male | 5.3% |
| 25-34 · female | 3.0% |
| 18-24 · unknown | 0.1% |
| Unknown · unknown | 0.0% |