Tom Willis WV US Inactive
Tom Willis Victory
Ad copy
Sen. Shelley Moore Capito was one of 15 Republicans who voted to incentivize radical “red flag” laws, which allow firearms to be confiscated based on anonymous tips. As our U.S. Senator, I will always protect Second Amendment rights and will never support legislation that undermines the constitutional right to keep and bear arms. Roll Call S. 2938 (2022): https://www.senate.gov/legislative/LIS/roll_call_votes/vote1172/vote_117_2_00242.htm
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 28.0/100 |
| Spend / Reach / Long. / Eff. | 0.0 · 43.0 · 4.1 · 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 | 16 (2026-04-25 → 2026-05-11) |
| Created | 2026-04-24 |
| Creative variants | 1 body · 0 headline |
| Platforms | facebook, instagram |
| Languages | — |
| Candidate | WV — Tom Willis |
| Surfaced by | “senate:Tom Willis” |
🧠 AI analysis
| Issue | guns |
| Framing | contrast comparison |
| Message type | persuasion |
| Emotional appeal | fear |
| Production tier | semi pro |
| Who pictured | candidate, opponent |
| Symbols | guns 2a |
| Call to action | none |
| Standout element | Dark desaturated image of opponent overlaid with ATF officers and red accusatory text 'CONFISCATE GUNS' |
| Tactic | The ad uses a sharp contrast structure — a bright, confident candidate image paired with a dark, ominous opponent panel — to drive a fear-based Second Amendment attack that frames a Senate vote on red flag laws as a direct threat to gun ownership. |
Where it ran (delivery by region)
| West Virginia | 91.0% |
| Virginia | 1.6% |
| Pennsylvania | 1.2% |
| Ohio | 1.1% |
| Maryland | 0.9% |
| Florida | 0.9% |
| North Carolina | 0.3% |
Who saw it (delivery)
| 18-24 · female | 0.2% |
| 18-24 · male | 1.0% |
| 25-34 · female | 0.4% |
| 25-34 · male | 2.1% |
| 25-34 · unknown | 0.1% |
| 35-44 · female | 1.0% |
| 35-44 · male | 3.7% |
| 45-54 · female | 3.4% |
| 45-54 · male | 10.8% |
| 45-54 · unknown | 0.1% |
| 55-64 · female | 7.6% |
| 55-64 · male | 23.7% |
| 55-64 · unknown | 0.1% |
| 65+ · female | 15.5% |
| 65+ · male | 30.2% |
| 65+ · unknown | 0.5% |
