📜 Content as Signal: Why Algorithms Hate Spam

About fifteen years ago, SEO was primitive: write the text and stuff «buy plastic windows» in 50 times. The robot saw high keyword density and pushed you to the TOP.

Today, attempting to fool the system this way will bring harsh penalties.

🧠 BERT and MUM — What These Are

Modern Google runs on machine learning. The key algorithms:

• BERT (since 2019) — understands word context. «Pass the ticket, I'll take it for you» vs «pass the ticket, I'll drive you» — the algorithm sees the different meaning
• MUM (since 2021) — understands multimodally: text + images + video. Can answer questions like «how to prepare for climbing Mount Adams if I've already done Mount Fuji»
• Gemini 3.5 Flash (since 2026) — replaced classic ranking. Understands intent in LLM style

They can analyze natural language. They understand synonyms, context, and meaning. Keyword stuffing = red flag for them.

👁 How Google Measures Quality — User Behavior

Google evaluates content quality through the lens of user behavior. How does it work?

This is called Bounce Rate. For Google, this is a clear error signal: the site didn't solve the problem. You'll be demoted.

And vice versa: if the person came in, read the article, looked at images, went to another page on the site — the system records a positive signal. This is Dwell Time.

📊 Main Behavioral Signals

• Bounce Rate — % of users who left immediately. Lower = better
• Dwell Time — how much time they spent. More = better
• Pogo-sticking — returned to results and clicked another. Worst signal
• Pages per session — how many pages they viewed. More = better
• Returning visitors — did they come back later. Very strong signal

✍️ How to Write So the Algorithm is Happy

You need to write text not for machines, but for people. Machines have long learned to understand human language — now they evaluate whether people liked it.

• Solve the customer's problem fast — main answer in the first 3 paragraphs
• Precise data — numbers, dates, specific models
• Tables and lists — structure helps both humans and AI Overviews
• Clear instructions — step-by-step, with code examples/screenshots
• No fluff — «in today's challenging times», «as is known», «let's consider» — the algorithm sees this as filler