Janet Mills US Inactive
Mills for Maine
URGENT: Maine is a virtual toss-up
Join Team Mills. Make a donation today.
Ad copy
2022 will be one of the most competitive elections in years, and political pundits are all saying that Maine is a virtual toss-up. Over the past three years, Janet Mills has expanded health care, made historical investments in our schools, and fought climate change. But Paul LePage and his special interest cronies are going to do everything they can to take Maine backwards. LePage has already raised tens of thousands of dollars from out-of-state mega-donors. With so much at stake, we need to fight back and keep up our momentum. Can you rush a $10 donation right now to help Janet win in November?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 38.3/100 |
| Spend / Reach / Long. / Eff. | 0.0 · 55.8 · 22.9 · 74.6 |
| Spend range | <USD 99 |
| Impressions | 10,000 – 14,999 |
| CPM (≈ $/1k impr) | USD 3.96 |
| Est. audience size | — |
| Days live | 84 (2022-03-31 → 2022-06-23) |
| Created | 2022-03-30 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | ME — Janet Mills |
| Surfaced by | “senate:Janet Mills” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | urgency |
| Production tier | unknown |
| Who pictured | none |
| Symbols | none |
| Call to action | donate |
| Standout element | Facebook content-removed placeholder screen shown as the ad image itself |
| Tactic | The ad leverages urgency and fear of a toss-up race to drive small-dollar donations, framing LePage and out-of-state money as an existential threat that requires an immediate $10 response. |
Where it ran (delivery by region)
| Maine | 32.9% |
| California | 7.2% |
| New York | 4.5% |
| Florida | 4.2% |
| Massachusetts | 3.4% |
| Michigan | 2.9% |
| Texas | 2.9% |
Who saw it (delivery)
| 18-24 · unknown | 0.1% |
| 65+ · female | 37.3% |
| 35-44 · male | 1.9% |
| 25-34 · female | 1.5% |
| 18-24 · male | 1.1% |
| 55-64 · male | 8.3% |
| 65+ · male | 26.5% |
| 25-34 · male | 2.2% |
| 45-54 · male | 2.6% |
| 55-64 · unknown | 0.3% |
| 18-24 · female | 0.4% |
| 55-64 · female | 11.5% |
| 65+ · unknown | 1.0% |
| 35-44 · female | 1.6% |
| 45-54 · unknown | 0.1% |
| 35-44 · unknown | 0.1% |
| 25-34 · unknown | 0.1% |
| 45-54 · female | 3.2% |
