Tom Willis WV US Inactive
Tom Willis Victory
Ad copy
Absolutely unbelievable. My opponent, Shelley Moore Capito, told reporters she is siding with Senate Democrats to defund ICE in the upcoming federal spending bill... a move that is completely out of touch with West Virginians. As your U.S. Senator, I will vote to fully fund ICE, secure our border, and stand up for law and order. West Virginia deserves a senator who puts public safety and the rule of law first, not reckless Washington politics.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 40.4/100 |
| Spend / Reach / Long. / Eff. | 25.8 · 68.1 · 1.9 · 65.7 |
| Spend range | USD 500–599 |
| Impressions | 40,000 – 44,999 |
| CPM (≈ $/1k impr) | USD 12.93 |
| Est. audience size | 100,001 – 500,000 |
| Days live | 8 (2026-02-05 → 2026-02-13) |
| Created | 2026-02-05 |
| Creative variants | 1 body · 0 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | WV — Tom Willis |
| Surfaced by | “senate:Tom Willis” |
🧠 AI analysis
| Issue | immigration |
| Framing | attack opponent |
| Message type | persuasion |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | opponent |
| Symbols | border, none |
| Call to action | none |
| Standout element | Shelley Moore Capito using a hair dryer to melt an ice block branded with the ICE agency logo — a staged visual metaphor for 'defunding ICE' |
| Tactic | The ad uses a visually absurd but memorable image of the opponent literally melting ICE to make an abstract policy stance viscerally concrete and damaging for a conservative West Virginia audience. |
Where it ran (delivery by region)
| West Virginia | 92.7% |
| Ohio | 0.9% |
| Virginia | 0.9% |
| Florida | 0.9% |
| Pennsylvania | 0.9% |
| Maryland | 0.7% |
| Kentucky | 0.6% |
Who saw it (delivery)
| 18-24 · female | 0.4% |
| 45-54 · male | 8.9% |
| 65+ · male | 21.7% |
| 65+ · female | 24.8% |
| 55-64 · unknown | 0.1% |
| 55-64 · male | 16.4% |
| 55-64 · female | 12.1% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 5.0% |
| 18-24 · male | 1.8% |
| 35-44 · unknown | 0.0% |
| 35-44 · male | 3.8% |
| 35-44 · female | 1.8% |
| 25-34 · unknown | 0.0% |
| 25-34 · male | 2.0% |
| 25-34 · female | 0.7% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.2% |
