SEOs and web savvy site owners are no strangers to referral spam, but recently a new type called language spam has sprouted up like a weed.
For many websites, the language section of Google Analytics is being spammed with the following message: “Secret.ɢoogle.com You are invited! Enter only with this ticket URL. Copy it. Vote for Trump!”.
Here’s an example of what it looks like:
The first signs of language spam were seen on November 8th, and I can confirm from looking at the unfiltered views of a number of the sites I monitor that it is still ongoing (despite the dated political message included in it).
As webmasters began to catch on to language spam and figure out ways to filter it out, another type of referral spam has manifested itself. This other type of spam makes it look like many authoritative domains are sending traffic your way. Some of the referring domains include Lifehacker, Reddit, and The Next Web. However, they’re entirely innocent in this matter and are simply a victim of clever hackers.
You can clearly see in this Reddit thread that it is a known issue affecting many site owners. You can also see in your own Google Analytics reports that the language associated with these referral sources matches that of the language spam mentioned earlier. It’s safe to say the two types of spam are linked.
There appears to be no consistency in how spammers are engaging in this new and unique type of analytics spam. Some sites were hit worse than others, some sites are still being hit, for others it is going away on its own.
There are two ways referral spam gets sent to a website. One is through bots that are programmed to visit a site and appear like a legitimate visitor. The other way is through bots that are programmed to send artificial hits to Google Analytics servers.
There’s no way to stop referral spam completely, but there are ways to filter it out of your Google Analytics data. Georgi Georgiev at Analytics Toolkit has put together a tutorial for blocking out all types of spam mentioned in this post.
Georgiev goes over how to block referral spam both manually and with automated tools. If you only mange one or a handful of sites, applying the filter manually is recommended. Using automated tools is recommended if you’re responsible for managing a large number of sites.
I applied the manual filter to a client site today and I can tell you it is an easy procedure that will not take any more than 10–20 minutes of your time. I’ll briefly recap the steps I took.
Filtering Out Language Spam
Georgiev has devised a filter that will remove traffic from your Google Analytics reports where the language dimension contains 15 or more symbols. In addition, traffic from sources that contain invalid characters in the language field will also be filtered out.
“Since most legitimate language settings sent by browsers are 5–6 symbols and rarely is there traffic with 8–9 symbols in this field, it should only filter out language spam,” says Georgiev.
Here are the steps to follow:
- Visit your Google Analytics reporting dashboard
- Click on ‘Admin’ in the top navigation menu
- On the far right hand side, click on the option that says ‘View Settings’
- Click on ‘Filters’ on the left-hand menu
- Click on the red button that says ‘+ Add Filter’
- Change Filter Type to “Custom”
- Apply settings as shown in the screenshot below (for your copying and pasting convenience: .{15,}|\s[^s]*\s|\.|,|\!|\/ )
After applying these settings, click on ‘Verify this filter’ at the very bottom. If the filter was set up correctly, it should show you how many instances of referral spam could have been blocked out in the past several days. If you can verify the filter works, click on Save and you’re done.
Unfortunately applying filters does not remove historical referral spam, but it does ensure this type of spam will be filtered out going forward.
Referral spam can never be permanently removed because spammers will keep coming up with new ways to get around the latest filters. That’s why it’s important to monitor your Google Analytics account regularly for any oddities or inconsistencies.
