Spam is back in search. And in a big way.
Honestly, I don’t think Google can handle this at all. The scale is unprecedented. They went after publishers manually with the site reputation abuse update. More expired domain abuse is reaching the top of the SERPs than at any time I can remember in recent history. They’re fighting a losing battle, and they’ve taken their eye off the ball.
A few years ago, search was getting on top of the various spam issues “creative” SEOs were trialling. The prospect of being nerfed by a spam update and Google’s willingness to invest and care in the quality of search seemed to be winning the war. Trying to recover from these penalties is nothing short of disastrous. Just ask anybody hit by the Helpful Content update.
But things have shifted. AI is haphazardly rewriting the rules, and big tech has bigger, more poisonous fish to fry. This is not a great time to be a white hat SEO.
TL;DR
- Google is currently losing the war against spam, with unprecedented scale driven by AI-generated slop, and expired domain and PBN abuse.
- Google’s spam detection monitors four key groups of signals – content, links, reputational, and behavioral.
- Data from the Google Leak suggests its most capable detection focuses on link velocity and anchor text.
- AI “search” is dozens of times more expensive than traditional search. This enormous cost and focus on new AI products is leading to underinvestment in core spam-fighting.
How Does Google’s Spam Detection System Work?
Via SpamBrain. Previously, the search giant rolled out Penguin, Panda, and RankBrain to make better decisions based on links and keywords.
And right now, badly.
SpamBrain is designed to identify content and websites engaging in spammy activities with apparently “shocking” accuracy. I don’t know whether shocking in this sense is meant in a positive or negative way right now, but I can only parrot what is said.
Over time, the algorithm learns what is and isn’t spam. Once it has clearly established signals associated with spammy sites, it’s able to create a neural network.
Much like the concept of seed sites, if you have the spammiest websites mapped out, you can accurately score everyone else against them. Then you can analyse signals at scale – content, links, behavioral, and reputational signals – to group sites together.
- Inputs (content, linking reputational and behavioral signals).
- Hidden layer (clustering and comparing each site to known spam ones).
- Outputs (spam or not spam).
If your site is bucketed in the same group as obviously spammy sites when it comes to any of the above, that is not a good sign. The algorithm works on thresholds. I imagine you need to sail pretty close to the wind for long enough to get hit by a spam update.
But if your content is relatively thin and low value add, you’re probably halfway there. Add some dangerous links…
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