Neural Networks Assist In Combating Negative SEO TACTICS

In earlier articles I've deiscussed how the custom neural networks I've developed to assist with generic pattern recognition were used to help construct a seperate series of neural networks which power a software package I designed to help advertisers accurately identify their target demographic and construct advertisements that will be relenvat for said demographic. 

 

We experimented with the Facebook page of Indie Review Magazine, and with the help of the software, we were able to raise the page likes from around 100 to over 20k in a very shor period of time. At the peak we were average a rate of 2.5k likes a day. 

 

A significant dip in ad performance was observed when the Magazine's page was attacked by negative SEO tactics employed by a violent stalker. 

 

These negative SEO tactics had a significant impact on the page's fan engagement and advertising performance. 

 

Prior to the issues with the stalker, the page likes were growing at an average rate of 1.5k likes a day and peaking at 2.5k likes on more than one occassion. 

 

Following the negative attacks on the page the likes slowed to around 20 a day, ad performance plunged, and we were actually able to obtain better results for fan engagement by turning the ads off on our page. 

 

We are still in the process of investigating the impact the negative SEO tactics have had on our page, but it does seem that now we have been able to develop an advertising strategy that allows us to manuever around these attacks. 

 

We will be halting all advertising efforts so that we can focus on completely analyzing the date we've collected thus far, and we will provide upates on the progress of this project via figshare. 

 

You can contact me at Jamie@ITSmoleculardesign.com for more information about this project.