Abdul for U.S. Senate
Become a founding donor of our campaign today!
🚨Big news: the Republicans are SCARED of this campaign. The NRSC just attacked me by comparing me to Zohran Mamdani and calling us “anti-American.” Why? Because we’re both proud Muslim Americans who aren’t afraid to fight for what people deserve. I believe healthcare is a human right. That corporations shouldn’t buy our democracy. And that no one should go broke trying to live a decent life. If that’s considered “anti-American,” maybe the NRSC needs to rethink what this country stands for. They’re right to be worried — because we’re building a people-powered movement that can win in Michigan. If you’re with me, will you chip in $10 to my campaign today to build our grassroots momentum? We can’t do it without you. Thank you.
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| Overall score | 42.9/100 |
| Spend / Reach / Long. / Eff. | 30.5 · 72.4 · 3.0 · 65.7 |
| Spend range | USD 800–899 |
| Impressions | 60,000 – 69,999 |
| CPM (≈ $/1k impr) | USD 13.07 |
| Est. audience size | — |
| Days live | 12 (2025-06-26 → 2025-07-08) |
| Created | 2025-06-26 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MI — Abdul El-Sayed |
| Surfaced by | “senate:Abdul El-Sayed” |
| Issue | healthcare |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | fear |
| Production tier | semi pro |
| Who pictured | none |
| Symbols | none |
| Call to action | donate |
| Standout element | Screenshot of NRSC tweet used as proof of Republican fear and anti-Muslim bias |
| Tactic | Judo-flip tactic turns an opponent attack into a fundraising rallying cry by framing Republican criticism as validation of the campaign's threat and Muslim American identity as a badge of pride. |
| New York | 14.8% |
| California | 14.4% |
| Michigan | 9.8% |
| Texas | 8.8% |
| New Jersey | 6.2% |
| Illinois | 6.1% |
| Florida | 4.2% |
| 18-24 · female | 1.2% |
| 45-54 · male | 9.7% |
| 65+ · male | 2.7% |
| 65+ · female | 2.5% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 3.7% |
| 55-64 · female | 2.9% |
| 45-54 · unknown | 0.3% |
| 45-54 · female | 6.0% |
| 18-24 · male | 3.4% |
| 35-44 · unknown | 0.5% |
| 35-44 · male | 23.9% |
| 35-44 · female | 13.7% |
| 25-34 · unknown | 0.4% |
| 25-34 · male | 19.5% |
| 25-34 · female | 9.1% |
| 18-24 · unknown | 0.2% |
| 65+ · unknown | 0.1% |