Abdul for U.S. Senate
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Please stop scrolling for just a moment and check the scoreboard. Abdul is unique in this race. He’s the only candidate who has never taken a dime from corporate PACs, always rejected AIPAC, stands for Medicare for All, and is a ‘yes’ on abolishing ICE. That’s why polls show Abdul is the top choice for young people and voters of color in Michigan — two groups we’re going to need to turn out for Democrats if we want to beat Mike Rogers and keep this vital U.S. Senate seat in November. If you agree on all these positions, will you donate before our end-of-quarter deadline to support Abdul? Your donations are extremely important to helping us win Michigan so Abdul can go to Congress to fight against Trump and fight for us.
↗ View in Meta Ad Library (the live creative — image/video/layout)
| Overall score | 57.9/100 |
| Spend / Reach / Long. / Eff. | 59.4 · 99.5 · 7.2 · 65.6 |
| Spend range | USD 10,000–14,999 |
| Impressions | 900,000 – 999,999 |
| CPM (≈ $/1k impr) | USD 13.16 |
| Est. audience size | 50,001 – 100,000 |
| Days live | 27 (2026-03-04 → 2026-03-31) |
| Created | 2026-03-04 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MI — Abdul El-Sayed |
| Surfaced by | “senate:Abdul El-Sayed” |
| Issue | immigration |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | candidate, opponent |
| Symbols | none |
| Call to action | donate |
| Standout element | green checkmarks vs. red X's scorecard pitting Abdul against two named Democratic primary rivals on four progressive litmus-test issues |
| Tactic | Uses a stark visual scorecard to make Abdul the obvious winner in a primary contrast attack, leveraging progressive credibility markers to drive fundraising urgency. |
| Michigan | 16.7% |
| California | 12.6% |
| New York | 6.4% |
| Florida | 5.5% |
| Texas | 5.0% |
| Illinois | 4.6% |
| Pennsylvania | 3.3% |
| 18-24 · female | 2.2% |
| 45-54 · unknown | 0.2% |
| Unknown · male | 0.0% |
| Unknown · female | 0.0% |
| 65+ · unknown | 0.3% |
| 65+ · male | 10.9% |
| 65+ · female | 14.6% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 6.8% |
| 55-64 · female | 9.7% |
| 45-54 · male | 6.5% |
| 18-24 · male | 1.9% |
| 45-54 · female | 9.4% |
| 35-44 · unknown | 0.2% |
| 35-44 · male | 8.5% |
| 35-44 · female | 12.1% |
| 25-34 · unknown | 0.2% |
| 25-34 · male | 7.1% |
| 25-34 · female | 9.1% |
| 18-24 · unknown | 0.1% |
| Unknown · unknown | 0.0% |