Dakarai Larriett for Senate US Inactive
ELECT DAKARAI LARRIETT
Ad copy
“Elon Musk’s campaign contributions have been accompanied by a series of spectacular defeats over the past year, including the loss of Wisconsin Supreme Court candidate Brad Schimel,” Larriett added. “I’m looking forward to going head-to-head with Barry Moore this November!” Read the full story: https://www.alreporter.com/2026/04/21/u-s-senate-candidate-craig-jelks-slams-moore-over-musk-donations/
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 34.0/100 |
| Spend / Reach / Long. / Eff. | 25.8 · 50.6 · 6.9 · 52.8 |
| Spend range | USD 500–599 |
| Impressions | 7,000 – 7,999 |
| CPM (≈ $/1k impr) | USD 73.27 |
| Est. audience size | — |
| Days live | 26 (2026-04-23 → 2026-05-19) |
| Created | 2026-04-23 |
| Creative variants | 1 body · 0 headline |
| Platforms | facebook, instagram |
| Languages | — |
| Candidate | AL — Dakarai Larriett |
| Surfaced by | “senate:Dakarai Larriett” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | contrast comparison |
| Message type | persuasion |
| Emotional appeal | urgency |
| Production tier | semi pro |
| Who pictured | candidate |
| Symbols | money cash |
| Call to action | learn more |
| Standout element | Screenshot of Alabama Political Reporter article lending third-party credibility to the attack on Musk-linked donations |
| Tactic | The ad borrows credibility from local news coverage to frame opponent Barry Moore as tainted by Elon Musk's losing-record campaign money, using a trusted journalistic source to do the negative work for the candidate. |
Where it ran (delivery by region)
| Alabama | 97.4% |
| Mississippi | 1.4% |
| Georgia | 0.8% |
| Tennessee | 0.5% |
Who saw it (delivery)
| 18-24 · female | 0.2% |
| 18-24 · male | 0.5% |
| 25-34 · female | 1.7% |
| 25-34 · male | 0.9% |
| 35-44 · female | 2.3% |
| 35-44 · male | 1.6% |
| 35-44 · unknown | 0.0% |
| 45-54 · female | 4.6% |
| 45-54 · male | 2.7% |
| 45-54 · unknown | 0.0% |
| 55-64 · female | 10.6% |
| 55-64 · male | 5.8% |
| 55-64 · unknown | 0.0% |
| 65+ · female | 44.2% |
| 65+ · male | 24.6% |
| 65+ · unknown | 0.2% |
