Search engine optimization, or SEO, has changed significantly in the past few years. Thanks to the power of machine learning and artificial intelligence, the only way to build a sustainable, long-term SEO strategy is to create content people want to share. To combat these new trends, we need to employ our own machine learning technology to find what works and perform modern SEO at scale.
In this post, we’ll look at step 8: associate.
Influencers and SEO
Before we delve into the how, let’s address influencers, social media, and SEO. First, I took a look at more than 150,000 URLs of every kind, from mass media to corporate pages to individual blog posts. What’s the relationship between social media sharing and equity-passing inbound links?
We see a strong correlation between total social shares on any social network and equity-passing inbound links. This passes the common-sense test as well – someone who finds a link that’s worth sharing will also probably find that link worthwhile to include in content they publish.
What the above doesn’t prove, however, is which comes first – causality. Does a link worth sharing find its way into a piece of content, or does a link in a piece of content find its way into social media?
While it’s something of a generalization, sharing a piece of content is significantly less work than embedding a link to content in media. A cat could accidentally tap the share button on a smartphone. Encoding a link into content requires a very lucky cat. Thus, it’s likely that if we generate mindshare among influential social media audiences, we’ll see links to our content also appear in other online content.
With this understanding, how do we determine who could share our content?
Enter Influencer Identification
Social media monitoring tools of all stripes are capable of identifying links to existing content. Logically, if we know who our audience is and the terms/phrases/topics they search for, we should be able to identify to shares and links to similar content.
Let’s say I want to dig into attribution analysis as a term to rank for, based on steps 1-7 of our process. Who talks about attribution analysis? Who shares content about it? What do they share? Using influencer identification tools such as network graphs and centrality indexing (previously written about here), we identify who these people are.
Next, we map who talks to whom. I wrote custom software to extract this sort of thing, but it’s quite straightforward – make a network graph of your social media mentions. For network graphing, I recommend Gephi as it’s both free and relatively easy to learn.
Finally, we validate that indeed, this is the right crowd by running a topic model on the content they share.
This looks good – it’s on target with what I’d expect people talking about attribution analysis to be talking about, and marketing (my specialty) is prominent across the topics. We’ve got the right crowd.
Next: Who Shares Ranking Stuff
Now that we know who’s got the juice, we need to know whether they share links that eventually benefit from high rank. In the next post, we’ll mine that data. Stay tuned!
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