Social ad networks: click fraud & invalid traffic
Detect bots and invalid traffic across social networks, with every visit scored 0–100 per campaign and publisher.
Every social network, scored.
Pick a network for its scored breakdown, tracking tokens and the exact exclusion you can take.
On social platforms the invalid-traffic surface isn't publisher arbitrage — it's placement quality. The auction and creative are usually sound; the leakage is in specific placements and ad sets where automated or incentivised engagement slips into otherwise strong inventory.
How fraud works on social.
Where invalid traffic concentrates
Social invalid traffic tends to live in three places:
- Extended audience-network inventory — ads served inside third-party apps and sites, where quality varies far more than in the core feed.
- Engagement farms — operations that manufacture click-through signals on short-form placements (Reels, Stories) to monetise.
- Automated app environments that trip a pixel fire without any genuine user intent behind it.
The core in-feed placements usually perform; the leakage concentrates in a subset of placements and ad sets, which is why placement-level visibility matters more than a campaign average.
What ValidVisit scores
Because the social platforms auto-tag each click with a placement-aware id (e.g. `fbclid`), ValidVisit can attribute each scored visit back to the originating campaign, ad set, and placement. Each visit is weighed against 100+ independent data points — its network origin, the device behind it, and how the visitor behaves — combined into a single 0–100 quality score. That blend is especially diagnostic for automated app environments and engagement-farm tooling, so a pixel fire with no plausible human behind it is separated from a genuine but low-engagement visitor.
What you can actually do about it
Be clear-eyed about the levers: social platforms are largely walled, so you can't blacklist an individual bad publisher the way you can on a native or pop network. What you can do is turn off the audience/extended network (e.g. Meta's Audience Network), exclude the weak placement type where the platform allows it, shift budget toward the placements ValidVisit scores clean, and use the evidence to request invalid-click credits. ValidVisit rolls the score up by placement and ad set so those decisions are grounded — but if you want true sub-source exclusion, that lives on the self-serve native, push and pop networks.
How ValidVisit detects the fraud.
Data points → one score
Every visit is weighed against more than a hundred independent data points and reduced to a single, sortable 0–100 quality score.
Many angles, combined
Each data point is combined rather than checked in isolation, so a genuine human almost never trips enough of them to be flagged — and bots that beat one rarely beat the rest.
Proprietary, not a black box
The detection model is ours and stays that way. What you get is a clear verdict on every visit — not a single brittle rule you can game, and not an unexplained number you can't act on.
Pinned to the source
Every verdict maps to the campaign, publisher and placement that sent the click — so you know exactly which source to cut.
The same 0–100 score on every source, worst first — down to the placement you buy.
Social networks, answered.
Isn't social traffic already filtered by the platform?+
Platforms filter at their own definition of invalidity and report it in aggregate. ValidVisit gives you an independent, transparent second measurement — every visit scored 0–100 and attributed to the exact placement and ad set — so you can verify quality rather than take the aggregate on trust.
Where does social invalid traffic usually hide?+
Most often in extended audience-network inventory served across third-party apps, and in engagement-farm activity on short-form placements. Both concentrate in specific placements, which is why ValidVisit attributes scores at the placement and ad-set level.
Find the bots in your Social networks spend.
See which publishers and placements send real buyers vs bots — every visit scored 0–100, worst first.
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