Archive for the ‘Content Filtering’ Category

Final Post

October 2, 2009

No – I’m not dead yet!!

This is a final post at Tigerstail.wordpress.com because I am tired of seeking knowledge and bitching about that which is. It is time to use my skills to develope the solutions to all of the problems I have discovered.

Join Me at jimmicap.wordpress.com

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Beyond Google – Viral Traffic

February 6, 2008

If your discussing matters which are touchy to our government, you really have to expect their best effort at suppressing the information. It’s no secret that Google is cooperating with China in suppressing knowledge on a broad range of topics. It’s also obvious that as a socially responsible corporation, they probably hide or fail to list content on a broad range of topics. When they fail to list a site page, they sometimes let you know that the supporting documentation is at chillingeffects.org but only when the content is totally illegal.

For legal but sensitive material, they don’t really acknowledge content filtering but when caught, just release a statement about unintended errors which led to technical difficulties and an inability to find a specific YouTube movie etc. In our particular case, serious content filtering started at my Fatsavage blog with a condemnation of a known kiddie porn site, youngerbabes.com, which is still online. The technical proof of content filtering is offered on this blog.

Of course the immediate effect of content filtering is that your traffic gets slammed and you lose visitors, but there is an obvious solution, just keep on writing. My traffic at fatsavage has started rising again, and the site is ranked in the top million of 20 million sites monitored by quantcast.com. Yeah, I get more traffic if I avoid censored topics, but I’m not a singing caged bird.

Interestingly, my new exulted status is almost independent of Google referrals (Correlation Coefficient 33%) and much more dependent on loyal visitors.

This post is being published at both Fatsavge and Tigerstail Blogs, not because I’m lazy, but because I want the information to get out. If Google were bothering to feed my blog, this would almost certainly get this post de-listed for laziness or plagiarism. However, since they are no longer doing a great job for my traffic, it doesn’t matter which rules I break.

Content Filtering the Fatsavage.

January 28, 2008

Just because your site traffic drops, it does not mean that their is evidence of Content Filtering. In my last post, I described two blogs which are so dormant that their are no visitors and the information is so stale that there are few Google referrals. Still, this type of stagnant site does not happen overnight. It’s like turning an oven up or down. If you turn it up to speed cooking, it will take 15 minutes or so before you see the obvious effects and when you turn it off, the meal will keep on cooking as long as the temperature is above 140 Fahrenheit.

Following the procedure outlined in the last post, we can check for a stable relationship between site visitors due to Google referrals, regular visitors and how much of a viral effect there is. It’s pretty obvious where the break in the following data is, but even this part of the analysis does not prove that a sudden change occurs, it just proves there is a stable relationship.

Google referrals———–Total Visitors
141———————–291
113———————–231
123———————–250
157———————–340
102———————–231
45————————115
33———————— 81
37————————103
37———————— 92

The relationship is as follows with an incredible correlation coefficient (R squared) of 99%.

visitors equals 1.93(Google Referrals) plus 23.4(Regulars)

At this point, we know we have a stable relationship between visitors and Google referrals and the drop is obvious but is it significant enough to prove Google Content Filtering.

Well for the 21 days prior to the day with 340 visitors there had been a relatively constant rise starting from 112 visitors a day. The equation was:

Visitors equals 7(Day) plus 109(Regulars)

Now the correlation coefficient was somewhat low at only 67% but considering day to day variations in frequency of posting and the strength of the content, that is still a strong relationship. The weaker correlation lead to large Standard Error of the estimate of 32. Putting this in layman’s terms on a day where you expected 200, 95% of the time it would be between 136 and 264, and 99% of the time it would be between 103 and 297.

This broad range seems like we are shooting at the broad side of a barn but after the 24th day when we would have expected 277 or above 180 at the 99% confidence level we only had 115 visitors and after that it got even worse.

It’s pretty obvious, that there was a change and my most popular post at the time could no longer be found short of a direct entry of the title which was “Youngerbabes.com Hack This Site”. I can’t figure out why a rant about a know kiddie porn site which is still on line should have been blocked unless it’s protected government property.

Google Content Filtering in America!

January 26, 2008

If content filtering exists in America, there ought to be a way to prove it one way or the other. Any experiment should be open to the public with all assumptions identified and the experiment should be reproducible and have a scientific evaluation criteria.

As a working assumption, there should be three elements to the number of site visitors when averaged over time. First, there should be some measure or regular visitors who would average out over time to some relatively constant value. Next there should be a feed from the search engines which is a factor of the public interest in your topic. Also, there could be a viral effect where visitors recommend you to others and therefore one site visit by Google referral or one regular visit may translate into any number of visits. (This is probably highest with jokes and politics and lowest with technical). However, if both groups (regulars and referrals from Google) recommend you to about the same number of people, this doesn’t alter the mathematical approach.

For the past three weeks, this site has varied around 41 to 87 visitors a day with an average around 60 a day but WordPress only holds the search engine data for a week. So I have the following Google Data and site visitors.

Google Referrals—–Visitors
44——————53
51——————67
44——————64
48——————62
50——————75
33——————48
40——————54

Now I ran the above data through an online Linear regression tool and came up with a very high correlation coefficient of 86% (R squared 75%). For a moment just think of a fat reduction pill where the weight you lost was 75% related to the pills you took and independent of all other variables like food and alcohol consumption and exercise or other variables.

The equation is visitors equals 1.3(Google Referrals) plus 4.1(regulars)

Obviously no viral effect and a small number of regular visitors, but if you need the technical information you come because of a Google search.

Now the data is drifting down because the site has not been very active for the month but the average for the data above is 60 with a two standard deviation range of 42 to 78. I would be hard pressed to make a case for effective Content Filtering based on this data set because there is no sharp break in the curve and the number of visitors for the past week is much the same as it’s been for the past three weeks.

Just for fun, I have two very dormant sites one in business (2.6 visitors a day) and the other in recreation (5.4 visitors a day). The equations and correlations are as follows:

visitors equals 1.4(Google Referrals) plus NO(regulars) correlation coefficient 95%
visitors equals 1.0(Google Referrals) plus 1.8(regulars) correlation coefficient 89%

Once again there is no sign of content filtering as the sites just suffer from a lack of interest, both mine in writing on the topic and visitors needing information on the topic.

Content filtering is an on-off switch which is turned on when the content gets out of hand so there should be signs of a dramatic drop in traffic.

At Fatsavage.wordpresss.com I have seen three cases of dramatic content filtering over the past 6 months with two in the past month. Start reading at the home page and work backwards to December 18, 2007. In that short month (about 12 posts), I managed to piss of the Feds twice and been slammed by provable content filtering. Now, I was attempting to be offensive with all of it, but I failed 10 times as only two posts were serious enough to get blocked.

Can you figure out which two?