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TWITTER: THE DARK SIDE’s BOTS vs. HUMANS Clicks Ratio. Not good for humans!

Over 95% of all traffic on Twitter is automated generated garbage, bots powered, sub-human world of pings and clicks, redirectors, spam and storages of endless bytes. All these activities cost money, waste energy and, yes, pollute!

Clicks or Click Fraud? Clicks Animation

Check out animation about millions of bots clicks show at as "statistics"

Twitter: The Dark Side Study

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Twitter: The Dark Side |’s BOTS vs. HUMANS Clicks Ratio

Previous discovery -- that underreports raw, total number of clicks delivered to their short URLS -- would not be such a bad thing if the clicks reports were valid, having actual humans behind the click, but this is far from the truth. When we compared’s statistics with two other reputable analytics services, StatCounter’s and Google Analytics the differences were astonishing. All really tells you is click count somewhere between the true raw click count and the valid human click count though they dress it up in a fancy graphical presentation.

For’s total of 1,064 clicks Google Analytics has shown 34 and StatCounter 36 unique visitors. Let’s split GA & SC analytics and take the average of 35 unique visitors for 1,064 “clicks”.

That data shows that only 3.29% of all clicks registered on were valid, unique human clicks and the astonishing 96.71% were coming from non-human entities. In this case over-reports the number of clicks for 96.71%. Phantom Clicks Statistics















We then embarked on a grueling process of confirming that a click, as registered in a raw data gatered by our own URL shortner,, belongs to either a human or a bot. Files are analyzed on a number of levels. Many user agents could be found in various on-line databases but bot detection that depends on the http USER AGENT HEADER is still a long ways away from being 100% accurate because there will always be new user agents that aren't in any of the files yet and hence might not be properly identified. Further malicious bots can easily spoof a USER AGENT HEADER, which is after all simply a string of text put into the http header by the user agent. A bot can claim to be anything as we shall show in our final experiment. does not provide RAW data, just their own, generic and misleading data thus the need for an accurate source of data as gathered by logs.


The bottom line is simply this; relying soley upon the USER AGENT HEADER for bot detection is completely unreliable. It is like asking criminals to voluntarily where a striped suit every time they go out of their house.

We dived into IPA (Internet Protocol address) and RDNS (Reverse Domain Server Name) of every click and checked each and every IPA with:

We also searched for more data about IPAs that were difficult to asses as bots or humans, we googled them, we read about them on forums, we checked blacklisted IPA and compared them with difficult to assess IPAs and at the end we made a call on each IPA that showed up in our logs.
The FULL LIST of User-Agents clicking on and our own URL Shortener,, and the logic behind every call is in our BOTS vs. HUMANS Analysis that can be found in our endnotes or by clicking here.

We realized that the truth on statistics provides is that it statistics are egregiously inaccurate at best and fraudulent at worst. presents users with phantom numbers that counts cyberspace’s ghosts and drones, robots and crawlers, presenting them as they are all humans, something they are not.


PART XIV:' s BOTS vs. HUMANS ratio by Google's AdSense



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