Tom Willis WV US Inactive
Tom Willis Victory
Tom Willis Visits Closed Cleveland-Cliffs in Weirton
Ad copy
Visited Weirton outside of the closed Cleveland-Cliffs plant. It’s clear that the consequences of unchecked dumping are real. 900 hardworking West Virginians lost their jobs because foreign competitors have been allowed to flood our market unfairly. We cannot stand by while American steelworkers and our domestic supply chains are undercut by unfairly priced imports. At the federal level, I will fight to ensure we have strong protections in place to stop dumping and keep good-paying jobs and American industry right here at home in West Virginia.
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Performance proxies
| Overall score | 33.0/100 |
| Spend / Reach / Long. / Eff. | 0.0 · 55.8 · 1.7 · 74.6 |
| Spend range | <USD 99 |
| Impressions | 10,000 – 14,999 |
| CPM (≈ $/1k impr) | USD 3.96 |
| Est. audience size | 1,001 – 5,000 |
| Days live | 7 (2026-02-23 → 2026-03-02) |
| Created | 2026-02-23 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | WV — Tom Willis |
| Surfaced by | “senate:Tom Willis” |
🧠 AI analysis
| Issue | jobs labor |
| Framing | issue advocacy |
| Message type | persuasion |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | candidate, workers labor |
| Symbols | factory industry |
| Call to action | none |
| Standout element | Candidate speaking at branded podium in front of shuttered industrial plant with snow-dusted ground, evoking economic devastation |
| Tactic | The on-location setting at the closed Cleveland-Cliffs plant provides visceral, visual evidence of job loss, lending authenticity and urgency to the candidate's trade protection message. |
Where it ran (delivery by region)
| West Virginia | 75.9% |
| Pennsylvania | 8.6% |
| Ohio | 5.0% |
| Florida | 1.8% |
| New York | 0.8% |
| California | 0.8% |
| Texas | 0.7% |
Who saw it (delivery)
| 18-24 · female | 1.7% |
| 45-54 · male | 6.8% |
| 65+ · male | 14.6% |
| 65+ · female | 22.0% |
| 55-64 · unknown | 0.2% |
| 55-64 · male | 9.8% |
| 55-64 · female | 12.4% |
| 45-54 · unknown | 0.1% |
| 45-54 · female | 9.1% |
| 18-24 · male | 1.9% |
| 35-44 · unknown | 0.2% |
| 35-44 · male | 5.8% |
| 35-44 · female | 6.0% |
| 25-34 · male | 4.4% |
| 25-34 · female | 4.8% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.2% |
