Mason County

Key demographics — Mason County, West Virginia Source: U.S. Census Bureau (2020 Decennial Census; 2018–2022 ACS 5-year estimates)

• Population: ~25,100 (2020 Census); gradual decline since 2010
• Age:
  • Median age: ~43–44 years
  • Under 18: ~20–22%
  • 65 and over: ~21%
• Gender: ~49% male, ~51% female
• Race/ethnicity (percent of total population):
  • White: ~94–96%
  • Black or African American: ~1–2%
  • American Indian/Alaska Native: ~0.3–0.4%
  • Asian: ~0.2–0.3%
  • Two or more races: ~3%
  • Hispanic or Latino (any race): ~1–2%
• Households:
  • Total households: ~10,200
  • Average household size: ~2.4
  • Family households: ~65–67% of households
  • Married-couple families: ~48–51% of households
  • Households with children under 18: ~25–27%
  • Owner-occupied housing rate: ~77–78%
  • Median household income: roughly mid–$40Ks to just under $50K

Insights:

• Demographics skew older than the national median, with a sizable 65+ share.
• Population and household trends point to slow decline and aging, typical of many rural WV counties.
• The county is predominantly non-Hispanic White, with small but present multiracial and minority populations.
• Household structure is oriented toward married-couple and family households, with relatively high homeownership.

• County context: Mason County, WV population ≈24,700 (2023 est.); land area ≈431 sq mi; density ≈57 residents/sq mi.
• Digital access: Roughly 3 in 4 households have a home broadband subscription; about 1 in 5 lack home internet. A notable share are mobile‑only, making smartphone email the default for many residents.
• Estimated email users: ≈17,200 adult email users (about 87% of ≈19,800 adults 18+), aligning with rural adoption patterns.
• Age distribution and usage:
  • 18–34: ≈19% of population; ≈95% use email → ≈4,500 users.
  • 35–64: ≈42%; ≈92% use email → ≈9,500 users.
  • 65+: ≈22%; ≈75% use email → ≈4,100 users.
  • Under 18 not counted; usage is intermittent and school‑platform dependent.
• Gender split: Population ≈50.6% female, 49.4% male; email usage is near parity → ≈8,700 women and ≈8,500 men use email.
• Trends and connectivity insights:
  • Email is commonly accessed via smartphones due to patchy fixed broadband in rural areas.
  • Lighter, mobile‑optimized messages perform better given variable speeds.
  • Workday daytime opens are strong among employed adults; 65+ growth is driven by healthcare portals and government communications.
  • Lower density and longer last‑mile runs elevate reliance on community anchors (schools, county offices) for list growth and digital engagement.

Mobile phone usage in Mason County, West Virginia — 2025 snapshot

Bottom line

• Mason County is more mobile-dependent than West Virginia overall. A larger share of households use cellular data as their primary or only internet, and coverage/performance is highly corridor-based (strong along US‑35/WV‑2 and in Point Pleasant; weaker in interior hollows).
• Age and income patterns amplify smartphone dependence among working‑age and lower‑income residents while pulling down adoption among seniors, creating a wider intra‑county usage gap than the statewide average.

User estimates and adoption

• Smartphone access: A clear majority of residents use a smartphone; among adults under 65, penetration is comparable to the WV average, while adoption among seniors is meaningfully lower than the state average due to an older age mix.
• Cellular data at home: Roughly two-thirds of households maintain a cellular data plan (smartphone/tablet hotspot), and a noticeably higher‑than‑state share are “mobile‑only” for home internet (cellular but no wired subscription). This mobile‑only share is a key divergence from statewide patterns.
• Multi‑line households: Line-per-person ratios are elevated in the county’s working‑age households (family plans, work phones), consistent with statewide rural patterns; single‑line prevalence is higher among seniors.
• Prepaid vs postpaid: Prepaid usage is more prominent than the WV average, reflecting price sensitivity and variable credit access; postpaid dominates among households with multiple lines or employer devices.

Demographic usage differences

• Age: Seniors (65+) are less likely to own smartphones and less likely to use mobile data heavily; younger cohorts (18–44) report heavy mobile data/video use and app‑centric communications. The county’s older age structure depresses overall smartphone penetration vs. WV, but the under‑45 cohort behaves similarly to state norms.
• Income/education: Lower‑income households are more likely to be smartphone‑dependent, to use unlimited prepaid plans, and to rely on tethering for home connectivity; higher‑income households more often pair mobile with wireline broadband and own multiple devices.
• Geography: Residents in river towns and along US‑35/WV‑2 have stronger 5G/LTE signal and higher median speeds; interior valleys/hollows see more LTE fallback, network variability indoors, and greater reliance on Wi‑Fi calling.

Digital infrastructure and coverage

• Network footprint: All three national carriers serve population centers; 5G low‑band is established along primary corridors and in Point Pleasant, with LTE prevailing elsewhere. Coverage remains topography‑constrained off the main roads, producing dead zones and slower uplink in the interior.
• Performance pattern: Median download speeds are competitive in town/corridor sectors and more volatile in rural interior sectors. Uplink performance and latency are the typical constraints for video calls and telehealth outside corridor areas.
• Tower density and backhaul: Macro sites cluster along the Ohio River and US‑35/WV‑2, with sparser site density inland. Backhaul follows highway/river fiber routes; segments off‑corridor lean on microwave backhaul, contributing to variable capacity.
• Fixed alternatives: Cable/fiber availability is spotty outside town; DSL is legacy. This limited wireline footprint materially raises the county’s mobile‑only household share versus the WV average.
• Public safety/priority: FirstNet coverage tracks the AT&T macro grid along primary corridors; emergency communications are strongest near towns and major routes.

How Mason County differs from the state

• Higher mobile‑only reliance: A larger fraction of households rely solely on cellular for home internet than the WV average, tied to gaps in affordable, performant wireline service.
• More pronounced urban‑rural performance divide: Corridor/town areas perform similarly to state averages, but interior areas see larger drops in consistency and speed than typical statewide.
• Older age structure impact: Countywide smartphone adoption rates are pulled down by a larger senior share, even though usage among working‑age residents mirrors state norms.
• Plan mix: Prepaid share is higher and unlimited data reliance is more common among mobile‑only households than the WV average.

Implications

• Service demand is “corridor‑dense”: incremental capacity upgrades along US‑35/WV‑2 and in Point Pleasant yield outsized benefits.
• Mobile is the de facto broadband for many: plans with robust hotspot allowances, better uplink, and indoor‑coverage solutions (femtocells/Wi‑Fi calling) address real needs.
• Closing gaps requires backhaul and siting in interior valleys: small‑cell infill or additional macro sites with improved backhaul would narrow the county’s larger‑than‑state performance gap.

Mason County, WV social media snapshot (2025)

Population baseline

• Residents: ~25,100
• Adults (18+): ~20,100
• Gender: ~50.9% women, 49.1% men

Overall adoption

• Adults using at least one social platform: 78% (15,700 adults)

Most-used platforms among adults (share of all adults; estimated local usage)

• YouTube: 74% (~14,900)
• Facebook: 68% (~13,700)
• Instagram: 34% (~6,800)
• TikTok: 28% (~5,600)
• Pinterest: 26% (~5,200)
• Snapchat: 20% (~4,000)
• X (Twitter): 15% (~3,000)
• LinkedIn: 13% (~2,600)
• Reddit: 12% (~2,400)
• WhatsApp: 8% (~1,600)

Age profile of social media use (adults)

• 18–29: ~90% use social; Instagram, TikTok, and Snapchat lead; Facebook mainly for school, sports, and local groups
• 30–49: ~84%; Facebook and YouTube dominate, Instagram is secondary, TikTok meaningful for entertainment
• 50–64: ~74%; heavy on Facebook (news, groups, Marketplace) and YouTube (how‑to, local content); Pinterest strong for projects/recipes
• 65+: ~56%; Facebook is primary (community updates, obituaries, church, schools), YouTube for news/how‑to; limited TikTok/Instagram

Gender breakdown among local users

• Overall users: ~52% women, 48% men (slightly higher female adoption)
• Platform skews: Pinterest heavily female; Facebook modest female majority; Instagram and TikTok slightly female‑leaning; Snapchat female‑leaning among younger users; Reddit and X male‑leaning; YouTube roughly even

Behavioral trends and local patterns

• Facebook is the community hub: high engagement with local news and weather, Mason County Schools, volunteer fire/EMS, churches, and Point Pleasant–area groups. Marketplace is a primary channel for buying/selling vehicles, equipment, and household items
• Groups outperform pages for reach: “yard sale/buy‑sell‑trade,” lost‑and‑found pets, and community alerts drive frequent interactions
• Video consumption is strong: YouTube for how‑to, repairs, hunting/fishing, and local sports; Facebook video for school events and meeting clips; TikTok/Reels popular under 35
• Local culture/content wins: high school sports, the Mothman Festival, Mason County Fair, river and outdoor content, and small‑business spotlights perform best
• Messaging norms: Facebook Messenger is the default for most adults; Snapchat dominates among teens/young adults; WhatsApp usage is low
• Timing: peak activity evenings 7–10 pm; weekends strong; spikes during weather events, closures, and breaking local news
• Mobile‑first usage: many residents rely on smartphones as primary internet access; shorter, captioned videos and image‑led posts perform better than long reads
• Cross‑county spillover: audiences naturally include nearby Gallia County, OH and greater Ohio River communities, so regional targeting works

Method note: Figures are 2025 estimates for Mason County adults derived from U.S. Census/ACS population structure and 2024 U.S. platform‑use rates (Pew Research Center and comparable national studies), adjusted for the county’s older, more rural profile.