Today, I'm very excited to announce that Moz's Spam Score, an R&D project we've worked on for nearly a year, is finally going live. In this post, you can learn more about how we're calculating spam score, what it means, and how you can potentially use it in your SEO work.
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Moz Spam score ToolHow does Spam Score work?
Over the last year, our data science
team, led by Dr. Matt Peters, examined a great number of
potential factors that predicted that a site might be penalized or banned by
Google. We found strong correlations with 17 unique factors we call "spam
flags," and turned them into a score.
Almost every subdomain in Mozscape
(our web index) now has a Spam Score attached to it, and this score is viewable
inside Open Site Explorer (and soon, the MozBar and
other tools). The score is simple; it just records the quantity of spam
flags the subdomain triggers. Our correlations showed that no
particular flag was more likely than others to mean a domain was
penalized/banned in Google, but firing many flags had a very strong correlation
(you can see the math below).
Spam Score currently operates only
on the subdomain level—we don't have it for pages or root domains. It's been my
experience and the experience of many other SEOs in the field that a great deal
of link spam is tied to the subdomain-level. There are plenty of
exceptions—manipulative links can and do live on plenty of high-quality
sites—but as we've tested, we found that subdomain-level Spam Score was the
best solution we could create at web scale. It does a solid job with the most obvious,
nastiest spam, and a decent job highlighting risk in other areas, too.
How to access Spam Score
Right now, you can find Spam
Score inside Open Site Explorer, both in the top metrics (just
below domain/page authority) and in its own tab labeled "Spam
Analysis." Spam Score is only available for Pro subscribers right
now, though in the future, we may make the score in the metrics section
available to everyone (if you're not a subscriber, you can check it out with
a free trial).
The current Spam Analysis page includes a list of subdomains or pages linking
to your site. You can toggle the target to look at all links to a given
subdomain on your site, given pages, or the entire root domain. You can further
toggle source tier to look at the Spam Score for incoming linking pages or
subdomains (but in the case of pages, we're still showing the Spam Score for
the subdomain on which that page is hosted).
You can click on any Spam Score row and see the details about which flags
were triggered. We'll bring you to a page like this:
Back on the original Spam Analysis page, at the
very bottom of the rows, you'll find an option to export a disavow file, which
is compatible with Google Webmaster Tools. You can choose to filter the file to
contain only those sites with a given spam flag count or higher:
Disavow exports usually take less than 3 hours to finish. We can send
you an email when it's ready, too.
WARNING: Please do not export this file and simply upload
it to Google! You can really, really hurt your site's ranking and there may be
no way to recover. Instead, carefully sort through the links therein and make
sure you really do want to disavow what's in there. You can easily remove/edit
the file to take out links you feel are not spam. When Moz's Cyrus Shepard disavowed every link to his own site, it took more
than a year for his rankings to return!
We've actually made the file not-wholly-ready for upload to Google in order
to be sure folks aren't too cavalier with this particular step. You'll need to
open it up and make some edits (specifically to lines at the top of the file)
in order to ready it for Webmaster Tools
In the near future, we hope to have Spam Score in the Mozbar as well, which
might look like this:
Sweet, right? :-)
Potential use cases for Spam
Analysis
This list probably isn't exhaustive, but these are a few of the ways we've
been playing around with the data:
Over time, we're also excited about using Spam Score to help improve the PA
and DA calculations (it's not currently in there), as well as adding it to
other tools and data sources. We'd love your feedback and insight about where
you'd most want to see Spam Score get involved.
Details about Spam Score's calculation
This section comes courtesy of Moz's head of data science, Dr. Matt
Peters, who created the metric and deserves (at least in my humble opinion) a
big round of applause. - Rand
Definition of
"spam"
Before diving into the details of the individual spam flags and their
calculation, it's important to first describe our data gathering process and
"spam" definition.
For our purposes, we followed Google's definition of spam and gathered
labels for a large number of sites as follows.
We performed the most recent data collection in November 2014 (after the
Penguin 3.0 update) for about 500,000 subdomains.
Relationship
between number of flags and spam
The overall Spam Score is currently an aggregate of 17 different
"flags." You can think of each flag a potential "warning
sign" that signals that a site may be spammy. The overall likelihood of
spam increases as a site accumulates more and more flags, so that the total
number of flags is a strong predictor of spam. Accordingly, the flags are
designed to be used together—no single flag, or even a few flags, is cause for
concern (and indeed most sites will trigger at least a few flags).
The following table shows the relationship between the number of flags and
percent of sites with those flags that we found Google had penalized or banned:
ABOVE: The overall probability of spam vs. the number
of spam flags. Data collected in Nov. 2014 for approximately 500K
subdomains. The table also highlights the three overall danger levels:
low/green (< 10%) moderate/yellow (10-50%) and high/red (>50%)
The overall spam percent averaged across a large number of sites increases
in lock step with the number of flags; however there are outliers in every
category. For example, there are a small number of sites with very few flags
that are tagged as spam by Google and conversely a small number of sites with
many flags that are not spam.
Spam flag details
The individual spam flags capture a wide range of spam signals link
profiles, anchor text, on page signals and properties of the domain name. At a
high level the process to determine the spam flags for each subdomain is:
Since the spam flags are incorporated into in the Mozscape index, fresh data
is released with each new index. Right now, we crawl and process the spam flags
for each subdomains every two - three months although this may change in the
future.
Link flags
The following table lists the link and anchor text related flags with the
the odds ratio for each flag. For each flag, we can compute two percents: the
percent of sites with that flag that are penalized by Google and the percent of
sites with that flag that were not penalized. The odds ratio is the ratio of
these percents and gives the increase in likelihood that a site is spam if it
has the flag. For example, the first row says that a site with this flag is
12.4 times more likely to be spam than one without the flag.
ABOVE: Description and odds ratio of link and anchor
text related spam flags. In addition to a description, it lists the
odds ratio for each flag which gives the overall increase in spam
likelihood if the flag is present).
Working down the table, the flags are:
On-page flags
Similar to the link flags, the following table lists the on page and domain name related flags:
ABOVE: Description and odds ratio of on page and
domain name related spam flags. In addition to a description, it lists
the odds ratio for each flag which gives the overall increase in spam
likelihood if the flag is present).
If you'd like some more details on the technical aspects of the spam score, check out the
video of Matt's 2012 MozCon talk about Algorithmic Spam Detection or the slides (many of the details have evolved, but the overall ideas are the same):
Blog source : http://moz.com/blog/spam-score-mozs-new-metric-to-measure-penalization-risk
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