GRAHAM FOR MAINE
I'm running against Susan Collins, AIPAC, Super PACs, and the entire DC establishment.
Janet Mills just got in this race, so you're going to be bombarded with fundraising texts and emails from Chuck Schumer and her campaign. They're going to be able to raise big money. Big money from corporate PACs. Big money from AIPAC. Big money from the establishment. So I need your help. I need your help because we are only raising money with small-dollar donations from people like you. And I am counting on you to chip in now if we're going to take on the corporate interests in Washington and defeat Susan Collins. If you can, please make a monthly donation now to our campaign to help me defeat Susan Collins, flip Maine blue, and take back the Senate for Democrats.
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| Overall score | 57.4/100 |
| Spend / Reach / Long. / Eff. | 69.6 · 100.0 · 1.1 · 58.9 |
| Spend range | USD 30,000–34,999 |
| Impressions | 1,000,000 – 0 |
| CPM (≈ $/1k impr) | USD 32.5 |
| Est. audience size | — |
| Days live | 5 (2025-10-14 → 2025-10-19) |
| Created | 2025-10-14 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | ME — Graham Platner |
| Surfaced by | “senate:Graham Platner” |
| Issue | economy taxes |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | anger |
| Production tier | diy amateur |
| Who pictured | candidate |
| Symbols | money cash |
| Call to action | donate |
| Standout element | candidate in a Dropkick Murphys hoodie speaking to camera outdoors — deliberately working-class, anti-establishment aesthetic |
| Tactic | Classic grassroots fundraising contrast tactic: frames opponent as flush with corporate and establishment money to create urgency and moral contrast, driving small-dollar donors to give now. |
| California | 10.5% |
| Maine | 9.8% |
| New York | 8.6% |
| Massachusetts | 8.0% |
| Pennsylvania | 4.0% |
| Washington | 3.9% |
| Illinois | 3.5% |
| 18-24 · female | 1.4% |
| 45-54 · male | 9.9% |
| 65+ · male | 5.9% |
| 65+ · female | 7.4% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 6.5% |
| 55-64 · female | 6.8% |
| 45-54 · unknown | 0.2% |
| 45-54 · female | 7.7% |
| 18-24 · male | 4.7% |
| 35-44 · unknown | 0.5% |
| 35-44 · male | 15.8% |
| 35-44 · female | 10.9% |
| 25-34 · unknown | 0.5% |
| 25-34 · male | 14.0% |
| 25-34 · female | 7.2% |
| 18-24 · unknown | 0.2% |
| 65+ · unknown | 0.2% |