Every analytics dashboard reports one click number. That number is almost always two different populations merged into one: people who tapped a link on purpose, and software that touched the same URL for a reason that has nothing to do with interest. Sorting out which is which matters more on link-in-bio pages and short links than almost anywhere else, because that is exactly where automated traffic concentrates.
Where bot traffic on a link actually comes from
Most bot traffic on a tracked link is not malicious. It is one of a handful of ordinary, well documented processes running in the background every time a URL gets shared.
Search engine crawlers, like Googlebot and Bingbot, follow links to index pages. They identify themselves by user agent and arrive on their own schedule, not tied to when you posted anything. Link preview bots fetch a URL the moment it is pasted into a chat or post, so the app can build a preview card with a title, image, and description. This is link prefetching, and on a fresh link it is usually the single largest source of non-human hits. Uptime and security scanners run inside corporate email systems and monitoring tools, checking that a URL is safe or reachable before or after a message reaches someone. Scrapers harvest page content or pricing and rarely identify themselves honestly. Click fraud is the rare, deliberately malicious case: traffic generated on purpose to drain an ad budget or inflate a number someone gets paid on.
Only the last category is actually trying to deceive anyone. The rest exist for reasons that have nothing to do with your link, which is exactly why they are so easy to mistake for real interest.
Bot traffic in analytics: the types that inflate a count
The table below separates the common categories by what they are actually doing, when they tend to show up, and whether they should worry you.
| Bot type | What it is doing | Typical timing | Worth worrying about |
|---|---|---|---|
| Search crawlers (Googlebot, Bingbot) | Indexing the destination page | Anytime, on its own schedule | No |
| Link preview or unfurl bots (iMessage, Slack, WhatsApp, Discord) | Building a preview card the instant a link is shared | Seconds after the link is pasted | No, but it inflates raw click counts |
| Uptime or monitoring bots | Checking a URL responds and loads correctly | Regular fixed intervals | No |
| Corporate email or antivirus link scanners | Pre-checking a link is safe before it reaches an inbox | Instantly, before the human ever opens the email | No, but confusing on email campaigns |
| Scrapers | Harvesting page content, prices, or structure | Anytime, sometimes in repeated bursts | Sometimes |
| Click fraud or paid bot traffic | Deliberately inflating a click count or draining a budget | Unnatural bursts, often from a narrow IP range | Yes |
How to filter bot traffic
There are three broad approaches. They are not equally reliable, whatever the marketing on a given analytics tool claims.
User agent matching is the cheapest method, and the one most free analytics tools default to. It reads the string a request identifies itself with and drops anything that names a known crawler. Easy to build, easy to defeat. A user agent is just text a request sends about itself, so anything actually trying to fake a click can claim to be a normal browser. It also occasionally punishes real people, since some privacy focused browsers and VPN clients ship generic or altered user agent strings.
Then there is IP reputation matching, which blocks known data center ranges and published bot IP lists. It catches more automated traffic. But it misses anything routed through a residential proxy, which is how most deliberate click fraud actually runs.
The most reliable option, and the hardest one to fake, is behavioral signals. Timing is the clearest tell: a click landing one to three seconds after a link is shared did not come from someone reading a message and deciding to tap it. The referrer and UTM pattern behind the click is the second, since bots frequently arrive with no referrer at all or repeat the exact same one. A tool that logs referrer, timestamp, and UTM data on every click, the way Raydar does on every raydar.bio link, gives you the raw material to spot these patterns yourself, even without a dedicated bot filtering feature. The full walkthrough for reading those patterns is in how to tell if clicks are real.
Is my traffic real, or is it bots
Nobody's traffic is one hundred percent human. That is normal. What matters is whether the bot share is big enough to change a decision, not whether it exists at all. A handful of preview bot hits on a link you posted in a group chat does not matter (nobody is fooled by what Slack's unfurl bot does, least of all you). A click count that is consistently double what Instagram's own bio link taps report for the same period is a real gap worth investigating.
Two numbers are worth pulling apart before you trust either one: total clicks and unique clicks. If total clicks run far ahead of unique clicks on a link that has not been shared widely, something other than distinct people is generating repeat hits. Combine that with a look at the referrer data on the suspicious clicks and the picture usually gets clear fast.
The part most explanations leave out
Bot filtering that relies only on a list of known bot names will always be behind. New tools do not announce themselves. The list gets updated after the fact, never before. What actually holds up over time is timing: when did the request arrive relative to when the link went live. A link posted to a public Instagram story ten minutes ago should not have fifty clicks with zero seconds of spread between them. That pattern is close to impossible for a real person to produce and close to inevitable for automated fetching, which is why timing distribution beats user agent strings as the strongest filter available to anyone without a dedicated fraud detection system.