ed by Virante, Inc. (now Hive Digital) , I wrote a post on Moz that outlined some simple ways to detect backlink manipulation by comparing one’s backlink. A profile to a model based on Wikipedia . At the time, I was limited in the research I could do because.
I was an API user, , measurements
And methodologies to identify anomalies in backlink profiles. We used these techniques to identify backlink manipulation with tools like Remove’em malaysia number data and. Penguin Risk, but they were always hampered by the limitations of the. APIs that users faced. Also, they didn’t scale. It’s one thing to aggregate all the backlinks for a site. Even a large one, and evaluate each individual link for source type, quality, anchor text, etc. Such reports can be accessed from dozens of vendors if you are willing to wait a few hours for the report to complete. But how do you do this for 30 trillion links every single day?
Since the launch of Link
Explorer and my residency at Moz, I’ve had the luxury of much less filtered data, giving me a much deeper, clearer picture of the tools available to backlink index maintainers to identify and combat manipulation. While I by no means intend to say that all manipulation can be detected, I do want to point out a few surprising ways to detect spam.
General procedure
You don’t need to be a data scientist or a math whiz to understand this simple process for identifying link spam. While there is certainly a lot of examples as technology advances math involved in measuring, testing, and building practical models, the general gist is clearly understandable.
The first step is to get a nice random
A sample of links from the web, which you can read about here . But let’s assume you’ve already done that step. Then, for any properties of those random links (DA, anchor text, etc.), you estimate what’s typical or expected. Finally, you look for outliers and see if they correspond to anything important – like sites that are manipulating the link graph, or sites that are unusually good. Let’s start with a simple example, link decay. Lacking access to deep metrics.
Link theft and link spam Lacking access to deep metrics
Link decay is the natural occurrence of links either moving off the web or changing URLs. For example, if you get links after sending fans data out a press release, you would expect some of those links to eventually disappear as the pages are archived or removed due to being outdated. And, if you were to get a link from a blog post, you would expect the link to be on the home page of the blog until that post is pushed to the second or third page by new posts.
But what if you bought your links?
What if you have a large number of domains and all the sites are linking to each other? What if you use PBNs? These links don’t go bad. Controlling your inbound links often means you prevent them from ever going down. So, we can make a simple assumption: