Sarah Adlakha for US Senate US Inactive
SARAH ADLAKHA FOR SENATE
Mississippi doctor running for Senate on anti-corruption
A Mississippi physician and businesswoman is running against incumbent U.S. Sen. Cindy Hyde-Smith in the Republican primary, arguing that Washington’s core problem is corruption rather than policy.
Ad copy
We must target corruption before we can expect real policy change. If you’re a Mississippi voter and want change, vote for Sarah in the Republican Primary this Tuesday, March 10th.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 36.8/100 |
| Spend / Reach / Long. / Eff. | 17.4 · 61.7 · 1.1 · 66.9 |
| Spend range | USD 200–299 |
| Impressions | 20,000 – 24,999 |
| CPM (≈ $/1k impr) | USD 11.09 |
| Est. audience size | — |
| Days live | 5 (2026-03-05 → 2026-03-10) |
| Created | 2026-03-05 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | MS — Sarah Adlakha |
| Surfaced by | “senate:Sarah Adlakha” |
🧠 AI analysis
| Issue | democracy elections |
| Framing | positive candidate |
| Message type | persuasion |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | candidate |
| Symbols | us flag, border |
| Call to action | vote |
| Standout element | Candidate in red blazer gesturing confidently in front of Mississippi-shaped patriotic banners with dual campaign slogans |
| Tactic | The ad leverages a local TV news article as credibility proof while pairing an energetic candidate image with anti-corruption messaging to position Adlakha as a reform outsider against an incumbent. |
Where it ran (delivery by region)
| Mississippi | 100.0% |
Who saw it (delivery)
| 18-24 · female | 0.6% |
| 45-54 · male | 6.0% |
| 65+ · male | 38.4% |
| 65+ · female | 20.8% |
| 55-64 · unknown | 0.1% |
| 55-64 · male | 15.8% |
| 55-64 · female | 6.1% |
| 45-54 · unknown | 0.0% |
| 45-54 · female | 2.0% |
| 18-24 · male | 1.9% |
| 35-44 · unknown | 0.0% |
| 35-44 · male | 3.0% |
| 35-44 · female | 0.8% |
| 25-34 · male | 3.2% |
| 25-34 · female | 0.8% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.4% |
