How Machine Learning Fixed LinkedIn Share Counts

On February 7, 2018, the social networking service LinkedIn made the following pronouncement:

The Share on LinkedIn plugin, also known as the inShare plugin, will no longer display the counter that shows the number of shares. 

The share count on its own doesn’t fully reflect the impact that a piece of content delivers, and we encourage publishers and other content creators to leverage the inShare plugin as a way to drive conversation and engage with members on LinkedIn.

Does this sound familiar? Twitter did the same thing:

The Tweet button counts the number of Tweets that have been Tweeted with the exact URL specified in the button. This count does not reflect the impact on Twitter of conversation about your content — it doesn’t count replies, quote Tweets, variants of your URLs, nor does it reflect the fact that some people Tweeting these URLs might have many more followers than others.

While none of these metrics should be key performance indicators or substitutes for well-attributed business impact, they do provide directional value. They also provide competitive value; comparing two pieces of content for their shareworthiness is a reasonable proxy when we have no access to competitors’ website traffic or other business metrics.

So, what do we do? How do we learn what content is working when the provided metrics go away?

Listen to how we solved this problem:

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As marketers, as data-driven professionals, we can’t just throw our hands up in the air when data goes missing. We must be clever, and use the powers of machine learning and AI to help us solve our problems.

Christopher S. Penn
Co-founder, Trust Insights