Bot Detection Without JavaScript: What My Blog Measured
A blogger analyzes their website traffic to distinguish between human readers and automated bot activity without using JavaScript. By comparing request headers and user-agent data, the author identifies discrepancies in analytics that suggest significant non-human traffic.
Why it matters
Understanding bot traffic is essential for website owners to accurately measure engagement and optimize content performance.
On my blog, network and request-header rules moved 277 of 372 browser-User-Agent requests out of the Browsers category: 74.5% . That gives me a much more useful account of the traffic arriving at my Cloudflare Worker. It has not established how many people read the site. Over the same two complete UTC days, the remaining 95 Browser HTML observations still differ from 14 Cloudflare Web Analytics page loads . The useful result is knowing which requests the rules separate, why they separate them, and where the evidence stops.
Comparing two counters exposed the problem with my first-party analytics : I had treated browser User-Agents as evidence of readers. The counters measure different events, so their disagreement is a starting point for investigation. It cannot, by itself, tell me which requests were automation.
The initial alarm came from this comparison, saved on September 3:
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