MFA site examples: what do made-for-advertising sites look like?
Part of MFA avoidance
The short answer
This guide sorts made-for-advertising sites into four fictional archetypes: slideshow arbitrage pages that split a story across many ad loaded screens, AI listicle farms, recipe pages buried under stacked ads, and news rewrites that copy other outlets' reporting. Each leaves different evidence, so a reviewer should know which pattern to test for and what to save.
Key takeaways
- The examples here are fictional composites; no real website is named or implied.
- Slideshow arbitrage is the easiest archetype to see: count the clicks and the new ads each click loads.
- Recipe ad stacks and news rewrites take the most work to confirm, because real publishers in those categories can also run heavy ads and cover the same stories.
- Each archetype needs its own proof: a click path, a source trail, a phone recording or a side by side text comparison.
- Record what the page did, with device, time and entry link, before giving it a label.
What do MFA sites look like?
The trade groups' 2023 definition, published by the 4A's (opens in a new tab) and the ANA (opens in a new tab), describes MFA sites by five traits: a high ad-to-content ratio, rapidly auto-refreshing ad placements, a high percentage of paid traffic, generic content and templated designs. This guide groups those traits into four recognizable kinds of site. Knowing the archetype tells a reviewer which test to run first.
| Archetype | What the reader meets | Main MFA trait | First test to run |
|---|---|---|---|
| Slideshow arbitrage | A teaser story split across 20 or more screens, each with fresh ads | Slide shows forcing clicks, with multiple ads | Count clicks to the payoff and ads loaded per click |
| AI listicle farm | Endless top ten lists with identical blurbs on unrelated subjects | Generic, templated content | Compare the structure of three lists side by side |
| Recipe ad stack | A real recipe hidden under sticky video, inline units and pop-ups | High ad-to-content ratio | Record a phone session from title to recipe card |
| News rewrite | Fresh looking local or trending news that adds nothing to the original | Non-unique, regurgitated articles | Trace each story to its source and compare text |
How does a slideshow arbitrage site work?
The 4A's lists "slide shows forcing visitors to click through multiple pages to access content, with multiple ads" under rapidly refreshing placements, and adds that buying paid traffic "is the primary cost driver of operating an MFA business." The slideshow is how that cost is recovered. A visitor bought through a social ad or a recommendation box clicks through screen after screen, and each screen is a new page view with a new set of auctions.
Fictional example
The 30 slide celebrity home tour
A sponsored link promises photos of a retired athlete's lake house. The landing page shows one photo, two sentences and a Next button, with a large banner above, two units beside the photo and a sticky ad along the bottom of the phone screen.
Each Next click loads a new URL with a new photo and a new set of ads. The lake house appears on slide 24 of 30. The final slide offers a row of more stories, each leading to another slideshow on a sister domain with the same template.
How to document a slideshow candidate
- Enter the way a visitor would. Note the sponsored link or referring page and keep its full URL, including tracking parameters.
- Count clicks to the promised content. Record the slide number where the headline's promise is actually delivered.
- Count ads per screen. Screenshot three screens and note how many ad slots load on each.
- Check whether each click is a new page. Watch the address bar; a new URL per slide means a new page view and new auctions.
- Follow the end of the gallery. Note where the last slide sends the visitor.
Not every gallery is arbitrage; a photo essay may need separate panels. The deciding evidence is the ratio of clicks to content and the paid entry path, covered in the traffic arbitrage guide.
What does an AI listicle farm look like to a reviewer?
Fictional example
The site that ranks everything
A fictional lifestyle domain publishes dozens of lists a day: best dog beds, best tax tips, best hair dryers, best retirement towns. Every list has ten items, a 40 word blurb per item that restates the item's name and a generic benefit, and the same closing paragraph with the subject swapped.
No list says how items were chosen or tested. Product blurbs link to shopping pages, and the space between items holds an ad unit every two paragraphs. The author page shows one name credited with expertise in pets, finance, home appliances and real estate.
Three tests separate this archetype from a real buying guide: a stated method (who tested what, for how long), matching headings and closing text across three lists, and one author covering unrelated beats. Claim tracing and sitemap checks are in our guide to AI generated content sites.
What does a recipe ad stack look like?
Fictional example
The weeknight casserole that never loads
On a phone, a fictional recipe page opens with a sticky video player that follows the scroll. Between the headnote and the ingredient list sit six inline units and a recommendation box. Halfway down, a full-screen ad slides over the page and must be dismissed.
The recipe itself is real and works. But reaching the recipe card takes more than a dozen screens of scrolling, and the card's step list jumps when a unit above it expands.
This archetype is a matter of degree, because genuine food publishers can run heavy ad loads too. The Coalition for Better Ads (opens in a new tab) gives a reviewer neutral vocabulary: on mobile web its standards name pop-up ads, ad density higher than 30 percent, full-screen scrollover ads, large sticky ads and sticky, pop-out video ads among the experiences that fall below the standard. Google's publisher policies separately bar its ads on screens with more ads or paid promotional material than publisher content (opens in a new tab). The ad density guide covers measurement.
The useful test is the reader's task. If a visitor can reach and follow the recipe, the page is ad heavy. If ads stand between the visitor and the recipe on every visit, the page is built around them; record which formats did it.
What does a news rewrite operation look like?
Fictional example
Local news with no local reporters
A fictional site styled as a regional news outlet posts 60 stories a day about towns across three states. Each story restates a city council notice, a police release or another outlet's article, with sentences reordered and some words swapped for synonyms.
There are no interviews, no photos taken by staff and no corrections. The about page gives no owner or address, and the same template and ad layout appear on sister domains covering other regions.
Google's replicated content policy (opens in a new tab) bars its ads on sites that "copy content from other sites, modify it slightly" by substituting synonyms, and on "mirroring, framing, scraping or rewriting of content from other sources without adding value." Its spam policies (opens in a new tab) define scraping as taking content from other sites, often by automated means, to manipulate search rankings. A reviewer proves the pattern by putting the rewrite and the original next to each other.
Which MFA archetypes are hardest to spot?
Difficulty depends on how closely an archetype resembles a legitimate category. A slideshow farm looks like nothing else; a news rewrite looks like a real local outlet until the stories are traced.
| Archetype | Difficulty | Why | What settles it |
|---|---|---|---|
| Slideshow arbitrage | Low | The click path is visible in one session | Clicks to payoff and a paid entry link |
| AI listicle farm | Medium | Clean layouts and plausible text pass a quick glance | Side by side structure and a missing method |
| Recipe ad stack | High | Real food publishers can carry heavy ads too | A phone recording showing ads blocking the recipe |
| News rewrite | High | Stories match real events and look current | A source trail showing nothing was added |
What evidence should be saved for each archetype?
Evidence has to let a colleague, a client or the publisher reach the same view later. Pages change and sponsored links expire, so save the session as well as the page. The Internet Archive's Save Page Now (opens in a new tab) gives a permanent URL for a single page, not its outlinks or the whole site, so pair it with your own screenshots.
Evidence packet for an archetype finding
- Slideshow: the entry link, the slide number where the promise is delivered and screenshots of three slides with their ads.
- AI listicle: three lists side by side with matching headings highlighted and the author page saved.
- Recipe ad stack: a phone screen recording from title to recipe card, with device and time noted.
- News rewrite: the candidate story, the original source and a highlighted comparison of the two texts.
- Every packet: the date, the device, the reviewer's name and an archived copy of at least one page.
- Every packet: what remains unknown and what would change the finding.
What does an archetype review look like on a real brief?
Fictional example
A Kansas and Missouri sewing brief
A fictional Wichita sewing machine and quilting retailer asks for an English display plan in Kansas and Missouri, beside quilting patterns, sewing tutorials and craft guild news. The 30 candidates include pattern blogs, a regional craft magazine and sites from a broad crafts category.
Three candidates match the slideshow archetype: a 25 screen gallery of quilts reached through a sponsored link, with the promised pattern on the last slide. Two match the listicle archetype, ranking sewing machines without any stated testing. The planner saves the packets and leaves all five out with the evidence attached.
A popular pattern blog looks like a recipe ad stack at first, with a sticky video and many inline units. The phone recording shows the pattern download is reachable in a few scrolls and never covered, so the planner keeps it and notes the heavy ad load.
How does PlacementGraph detect these page patterns?
Frequently asked questions
Can we use real domain names when training our team?
Keep named examples inside internal, access controlled training, tied to dated evidence the team collected, and label each as a finding on that date. Sites change owners and practices. For client decks and anything shared outside the team, fictional composites avoid labeling a business on stale evidence.
Is an archived screenshot enough to prove a site is MFA today?
No. An archive shows what a page looked like when captured and supports a finding about that date. Before excluding a site on old evidence, revisit current pages on a phone and a desktop and record whether the pattern remains.
What if a page only behaves like an archetype after a sponsored link?
Record both sessions. A site can show one layout to a direct visitor and a heavier one to a visitor arriving from a paid link. Save the full entry link, including its parameters, and repeat the visit with and without it before writing the finding.
Do these archetypes appear in apps and connected TV too?
The four archetypes on this page are web patterns. Apps and connected TV monetize through different formats and need checks suited to those environments, such as reviewing the app on a test device rather than in a browser. Do not apply a web archetype label to an app without app evidence.
Sources
- Leading trade groups define made for advertising websites (opens in a new tab)4A's (American Association of Advertising Agencies), September 26, 2023
- Leading Trade Groups Define “Made For Advertising” Websites (opens in a new tab)ANA (Association of National Advertisers), September 26, 2023
- The Better Ads Standards (opens in a new tab)Coalition for Better Ads
- More ads or paid promotional material than publisher-content (opens in a new tab)Google Publisher Policies Help
- Google-served ads on screens with replicated content (opens in a new tab)Google Publisher Policies Help
- Spam policies for Google web search (opens in a new tab)Google Search Central, last updated August 28, 2026
- Save pages in the Wayback Machine (opens in a new tab)Internet Archive Help Center
Researched and written by PlacementGraph Research. Read how we research, source and update these pages. Corrections: diana@placementgraph.com.