How does Dibz’ SPAM algorithm work?
Our tool is all about fetching quality results, which is why our spam algorithm analyses 17 SPAM signals that are of various levels of importance, and neatly organizes them in plain numbers for you to use in quality analysis.
When analyzing a certain website, we take into consideration everything from social metrics to domain authority and domain rating data.
This spam algorithm helps our tool divide your search results in two groups:
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top quality results
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all results
Also read: How can I get more relevant results in Dibz?
To choose between top quality and all results for a certain search, you need to:
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Go to search results
2. Click on the eyecon, and then tick title.
Dibz also, when possible, scrapes email addresses from the prospects in your search results so you can instantly get into contact with desired domains.
Although the results are sorted by our algorithm’s spam factor, you do have the option to sort and organize your data by your own criteria. You also have the opportunity to list your search results by the parameters of your own choosing.
Related: What is “Custom Search” in Dibz and How Do I Set it Up?
The graphs you see in the Spam column of the Search Results page are a representation of how close to your spam score limit a site is. The more colored columns you see, the spammier the website is, according to the criteria you have specified from the Spam Settings page.
You can see the individual spam factors that were triggered by hovering the cursor over the graph.
Seeing how you can assign more weight to certain spam factors than to others, it’s not uncommon to see prospects which triggered several factors actually being shown by the graph as less spammy than some which only triggered one.
Finally, you’ll also occasionally see a grayed-out graph, showing “not available” on hover. There are a number of reasons why these websites might be difficult to analyze for spam factors, from them using JavaScript elements in sections we’d need to examine, to untidy or at least unorthodox code and markup, however, not all of them are bad, and the fact that they were difficult to probe for information doesn’t necessarily mean you should just dismiss these prospects.
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