Why Taboola Ads Look So Spammy (How the Auction and Marketplace Shape the Feed)

Taboola ads can look spammy for a few structural reasons. Taboola says its algorithm decides where to recommend ads using click-through rate and cost per click, so headlines that earn more clicks get shown more, whatever their editorial quality. Its own content rules rate some clickbait-style and exaggerated titles as acceptable at a "Medium" safety level, which limits them to publishers that accept that level rather than banning them. And its marketplace is broad: Taboola says it works with tens of thousands of advertisers, from large brands to small performance marketers. The result is a feed where a Georgetown University ad can sit next to "Doctors Are Stunned by This One Household Item."

The auction rewards clicks, within policy limits#

Taboola says Realize runs an auction every time an ad placement is available, and that its algorithm uses two main factors to decide when and where to recommend an ad: click-through rate and cost per click. The higher either is, the more often the ad is shown. Taboola doesn't publish an exact formula, but the effect is that an ad earning more clicks per impression can win more delivery than a tamer ad with the same bid. An exaggerated, curiosity-driven headline may get more clicks than a plain one, though we have not measured that. Campaigns using conversion-based bidding (Taboola recommends Maximize Conversions) are also steered by predicted conversions. None of this ranks ads on editorial quality; that job falls to Taboola's content review and safety ratings, covered below.

The marketplace is genuinely mixed quality by design#

Taboola's advertiser base runs from large brands to small performance advertisers, agencies and arbitrage buyers; Taboola itself says it works with tens of thousands of advertisers. That breadth is part of the business model of an open marketplace that needs enough demand to fill a large volume of publisher inventory. The native ad supply chain can also include agencies, resellers and demand partners between the original advertiser and the final placement, which can make it harder for readers, and sometimes researchers, to see who is really behind an ad.

Taboola reviews ads before launch, but review has limits#

Taboola says every campaign item is reviewed and labeled before it goes live, using "multiple methods, including technical and manual processes," and its Trust Center says all distributed content is first manually reviewed and categorized by its Global Content Review Team. We can't verify how that works in practice or how consistent it is across countries. What Taboola's own rules do show is that review is not a quality filter in the sense readers might expect: its safety ratings classify items as High or Medium, and Medium covers content that is "mean-spirited, exaggerated, exploits insecurities," including clickbait and exaggerated titles, belly-fat images and photoshopped arrows. Medium-rated campaigns can still run, on the subset of publishers that accept them, and Taboola reserves the right to change a rating after launch.

Why the same widget can show both extremes#

It's genuinely normal to see a Flight Centre travel deal, a Georgetown University program ad, and a "This Household Item Ends Knee Pain" creative in the same content-recommendation feed, because they're often served through the same underlying auction infrastructure even though they represent completely different advertiser tiers and campaign types. Advertisers can choose very different setups: Taboola offers controls over sites and article topics and pre-bid brand-safety filtering through DoubleVerify or IAS, while a campaign without those restrictions can reach a wider range of publishers. We don't have data on which kinds of advertisers use which setup. From a single reader's perspective scrolling one page, all of it just looks like "Taboola," even though the buying strategies and quality bars behind each ad are quite different.

What advertisers can actually control#

Advertisers who want to avoid the roughest parts of the marketplace have real levers in Taboola's tools: blocking individual sites in the campaign's Sites report, targeting or excluding article topics, and pre-bid brand-safety and suitability filtering through DoubleVerify or IAS. None of this changes the auction's incentive to reward clicks, but it does let an advertiser trade the widest reach for a more controlled slice of the same network. Our guide on how Taboola ads work covers the placement and targeting controls in more depth, and who advertises on Taboola describes the advertisers and categories we captured, which is a useful check on the "it's all junk" impression a quick scroll can leave, as long as you read capture counts as coverage rather than market share.

Is it actually a fraud problem, or a taste problem?#

It's worth separating two different complaints that both get labeled "spammy." One is aesthetic: exaggerated, curiosity-driven headlines that are technically accurate but framed for maximum clicks. The other is a real compliance problem: outright false claims, ad cloaking, or ad fraud dressed up in native format. The first is a byproduct of the auction's incentives and isn't going away as long as clicks are the currency. The second breaks the published policies of the major networks we checked, including Taboola's and Outbrain's, and is worth documenting and reporting when you see it; Taboola says it reviews every piece of content readers flag.

How to see the actual mix instead of guessing from a scroll#

Casually scrolling a feed gives you a skewed sample, since you're seeing whatever the algorithm decided to serve you specifically, at that moment, based on your own browsing profile. Looking at the actual advertiser and vertical distribution across a large captured sample tells a different, more balanced story than "every Taboola ad is spam." In OpenAdLibrary's historical captures, health, finance and insurance were the largest classified Taboola categories, with ecommerce, software and home and garden close behind, and the captures include plenty of plainly branded advertising that doesn't stand out the way a sensational headline does. Those counts describe our capture coverage, not Taboola's whole marketplace.

If you want to check what has been captured before assuming a category is dominated by junk, OpenAdLibrary's Taboola ad spy tool lets you search and filter captured Taboola ads by advertiser, category and country on the Founders plan ($29.99/month), rather than relying on whatever your own feed happens to surface. The free plan shows the 20 newest Meta, TikTok and native ads.

The vertical concentration makes the pattern worse, not better#

Health, finance and insurance are prominent in native feeds, including in our captures across networks, and those are the categories where the gap between a legitimate offer and an exaggerated one is hardest for a casual reader to judge. A supplement claim, a "hidden government benefit," or a device that "doctors don't want you to know about" can use the same narrative structure whether the offer behind it is real and compliant or something shadier. These categories are often described as having strong performance-marketing economics (high payouts, broad appeal to older readers), which would attract both legitimate advertisers and aggressive performance marketers, but we have not measured margins or payouts. That mix is a plausible reason the "spammy" impression feels strongest in these categories.

The bottom line#

Taboola ads look spammy because the auction is built to reward click-worthiness and advertiser bid, not brand polish, and because the marketplace genuinely contains everything from premium direct-buys to thin arbitrage plays running side by side. That's a structural feature of an open, high-volume ad marketplace, not evidence that the whole network is a scam. Advertisers who care about the company they keep have real tools to opt out of the roughest inventory. We don't know how many use them, or how the rest of the inventory performs for those who don't.