Tom Willis WV US Inactive
Friends of Tom Willis
Ad copy
Ever wonder why we rarely see Craig back home? When Craig Blair isn’t voting to put children through sex-change experiments or giving money to ‘woke’ leftist corporations, he is stuffing his pockets with our hard-earned taxpayer money. Senator Craig Blair has been caught abusing his "duty days" giving himself a $100,000 salary for a $20,000 part-time job. It’s time to end the corruption. On May 14, vote Tom Willis for WV Senate in the upcoming Republican Primary. With President Trump in Washington D.C. and Tom Willis in Charleston, we’re going to drain the swamp once and for all.
↗ View in Meta Ad Library (the live creative — image/video/layout)
Performance proxies
| Overall score | 33.9/100 |
| Spend / Reach / Long. / Eff. | 11.9 · 50.6 · 10.7 · 62.5 |
| Spend range | USD 100–199 |
| Impressions | 7,000 – 7,999 |
| CPM (≈ $/1k impr) | USD 19.93 |
| Est. audience size | 100,001 – 500,000 |
| Days live | 40 (2024-04-03 → 2024-05-13) |
| Created | 2024-04-03 |
| Creative variants | 1 body · 0 headline |
| Platforms | |
| Languages | en |
| Candidate | WV — Tom Willis |
| Surfaced by | “senate:Tom Willis” |
🧠 AI analysis
| Issue | economy taxes |
| Framing | attack opponent |
| Message type | persuasion |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | opponent |
| Symbols | capitol statue, money cash |
| Call to action | vote |
| Standout element | AI-generated caricature of opponent bent backwards clutching a giant money bag in front of the state capitol with cash raining down |
| Tactic | The ad uses visceral visual exaggeration and alliterative nickname 'Crooked Craig' to brand the opponent as corrupt, pairing a comedic yet damning image with specific financial allegations to make the attack feel grounded and shareable. |
Where it ran (delivery by region)
| West Virginia | 81.4% |
| Maryland | 17.1% |
| Virginia | 1.5% |
Who saw it (delivery)
| 45-54 · unknown | 0.1% |
| 25-34 · unknown | 0.0% |
| 35-44 · unknown | 0.0% |
| 55-64 · unknown | 0.1% |
| 65+ · unknown | 0.2% |
| 35-44 · male | 4.8% |
| 65+ · female | 21.4% |
| 45-54 · male | 10.8% |
| 55-64 · male | 20.4% |
| 45-54 · female | 3.7% |
| 65+ · male | 26.6% |
| 55-64 · female | 9.9% |
| 25-34 · male | 0.8% |
| 35-44 · female | 1.0% |
| 25-34 · female | 0.1% |
