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TWITTER: THE DARK SIDE’s Clicks Validity Determination Experiment(s)

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 Clicks Validity Determination Experiment(s)

Fraudulent, malicious or robotic automatic clicks without an actual human behind the click have no value for an advertiser or outright cost him money. Due to the 140 characters per tweet limit, shortened URLs are almost the only links on Twitter thus the ideal prey for all kind of malicious activities.

The challenge of the project was to analyze and measure the number, the frequency, and the validity of clicks delivered by marketers, advertisers and humans on the Twitter’s platform using Twitter’s URL shortener of choice vs. our own URL shortener and we also are comparing and analyzing data with a help of StatCounter and Google Analytics analytic tools.


EXPERIMENT 1 . Establishing BOTS vs. HUMANS clicks ratio on Twitter

The purpose of this experiment was to compare various traffic sources analytics tools results on the same landing webpage vs.’s own statistics. We used different landing WebPages and tried to establish BOTS vs. HUMAN clicks ratio for each.

In this Experiment we firstly set-up a Twitter Demographic Poll using Poll Daddy and placed them on the two Landing Pages used for the Experiment: and

and then created short URLs for each long URL, for example:

http://bit.lyXRZik and or

Then, by tweeting & twinking we have simply asked: “Please, tell us, fellow Twitterers, how old are you?” For that purpose we used following Twitter accounts:
In the course of the experiment, in its two runs we tweeted a Total of 3,212 times.

Then we set-up Analytics on the Landing Pages where we used:

*StateCounter’s Java Script based hit counter and real-time detailed web statistics,

*Google Analytics’s new Java Script based ga.js tracking tool,

*Google’s AdSense with its own, independent analytics tool, and

* for our URL shortener we installed our own web log analytics and tracked all source IP addresses.

As mentioned earlier, in order to get a better insight into metrics We used – Twitter’s default URL shortener, and two other URL shorteners:

* – our own URL shortener,

* – advertising based URL shortener and its independent analytics.

The results are in the Table below, on the following page. Please note we run two sets of tweets: Fraudulent Clicks Analysis data is (Total Clicks, Valid Clicks, Total Ad Hits, Valid Ad Hits)
Please NOTE : Landing Pages 1 from the Experiment are mostly taken down. Landing Pages 2 are still live on the Internet. Fraudulent Clicks Analysis

The first important finding (as seen in RAW vs. columns) is that for total of 625 tweets our URL shortener registered 2,619 clicks while for total of 586 tweets Twitter’s URL shortener of choice registered 1,064 clicks, all in the second run as seen in the second table that gives a ratio of:

4.19 clicks per each’s tweet and

1.82 clicks per each’s tweet so the findings say that under-reports Total number of clicks by 55.56%!


Bitly Invisible Clicks










PART XIII: Digging Deeper into BOTS vs. HUMANS ratio on



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