Tom Willis WV US Inactive
Tom Willis Victory
Ad copy
Just last year, Sen. Shelley Moore Capito skipped the vote and killed S. 9 (2025) to weaken protections for girls' sports, but now... conveniently in an election year with a Republican-only primary... she’s pretending she stood for common-sense conservative values all along. West Virginians don’t need "Election Day conversions." We need leaders who are genuine, stand firm, and don’t rediscover their principles only when the political winds shift.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 27.2/100 |
| Spend / Reach / Long. / Eff. | 0.0 · 43.0 · 0.8 · 65.1 |
| Spend range | <USD 99 |
| Impressions | 3,000 – 3,999 |
| CPM (≈ $/1k impr) | USD 14.14 |
| Est. audience size | 100,001 – 500,000 |
| Days live | 4 (2026-01-19 → 2026-01-23) |
| Created | 2026-01-19 |
| Creative variants | 1 body · 0 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | WV — Tom Willis |
| Surfaced by | “senate:Tom Willis” |
🧠 AI analysis
| Issue | social security medicare |
| Framing | attack opponent |
| Message type | persuasion |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | candidate, opponent |
| Symbols | none |
| Call to action | none |
| Standout element | screenshot of candidate's own tweet embedded as the core message, lending authenticity |
| Tactic | The ad uses a screenshot-as-evidence framing to attack the incumbent's credibility on conservative values, positioning the challenger as the authentic conservative by exposing perceived opportunistic flip-flopping ahead of a Republican primary. |
Where it ran (delivery by region)
| West Virginia | 93.5% |
| Pennsylvania | 1.2% |
| Ohio | 1.2% |
| Maryland | 0.8% |
| Virginia | 0.6% |
| Florida | 0.4% |
| Kentucky | 0.4% |
Who saw it (delivery)
| 18-24 · female | 0.4% |
| 45-54 · male | 8.9% |
| 65+ · male | 23.5% |
| 65+ · female | 22.9% |
| 55-64 · unknown | 0.1% |
| 55-64 · male | 18.1% |
| 55-64 · female | 13.6% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 4.9% |
| 18-24 · male | 0.6% |
| 35-44 · male | 3.5% |
| 35-44 · female | 2.1% |
| 25-34 · unknown | 0.1% |
| 25-34 · male | 1.1% |
| 25-34 · female | 0.4% |
| 18-24 · unknown | 0.1% |
| 65+ · unknown | 0.1% |
