Abdul El-Sayed US Inactive
Abdul for U.S. Senate
Chip in now ➡️
Ad copy
I’ve got some pretty bad news. A Republican Super PAC is spending $45 million dollars in this race. Why? Because they’re threatened by the power of this movement. If they win this seat, Republicans will lock in a Senate majority and can then pass anything Trump puts in front of them. We’ve got to stop them, but here’s the deal: I’m not waiting for a $45 million check. Instead, this campaign is powered by grassroots supporters like you. Will you chip in $20 or whatever you’re able to help this campaign keep up our momentum?
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 43.4/100 |
| Spend / Reach / Long. / Eff. | 43.1 · 70.7 · 4.1 · 55.6 |
| Spend range | USD 2,500–2,999 |
| Impressions | 50,000 – 59,999 |
| CPM (≈ $/1k impr) | USD 49.99 |
| Est. audience size | — |
| Days live | 16 (2026-04-17 → 2026-05-03) |
| Created | 2026-04-16 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MI — Abdul El-Sayed |
| Surfaced by | “senate:Abdul El-Sayed” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | fundraising |
| Emotional appeal | fear |
| Production tier | diy amateur |
| Who pictured | candidate |
| Symbols | none |
| Call to action | donate |
| Standout element | Tight close-up selfie-style video of candidate mid-speech against urban skyline, evoking urgency and authenticity |
| Tactic | Classic grassroots fundraising fear appeal — warns of a $45M Republican Super PAC threat to create urgency and frame a small donor as the counterweight to big money. |
Where it ran (delivery by region)
| Michigan | 23.9% |
| California | 14.5% |
| New York | 9.6% |
| Texas | 5.0% |
| Illinois | 4.3% |
| Massachusetts | 3.6% |
| Washington | 3.3% |
Who saw it (delivery)
| 18-24 · female | 0.2% |
| 45-54 · male | 9.4% |
| 65+ · male | 13.9% |
| 65+ · female | 19.2% |
| 55-64 · unknown | 0.4% |
| 55-64 · male | 10.6% |
| 55-64 · female | 9.9% |
| 45-54 · unknown | 0.3% |
| 45-54 · female | 7.1% |
| 18-24 · male | 0.4% |
| 35-44 · unknown | 0.4% |
| 35-44 · male | 11.8% |
| 35-44 · female | 6.8% |
| 25-34 · unknown | 0.2% |
| 25-34 · male | 6.2% |
| 25-34 · female | 2.7% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.5% |
