How to Identify Fourquare Influencers by Analysing Check-in Data
When people use Foursquare to check-in to a location they often leave a visible trail of their likes and interests that can be analyzed using Social Media Analytics, especially if the check-in is echoed on Twitter (many Foursquare and Twitter accounts are linked, providing public access to check-ins of the linked accounts).
Segmenting Foursquare check-in data is much easier than segmenting other social media data as check-in data contains context, has some structure and is short, and I was able to take about 30% of the close to 20 million check-ins over the last 3 months and put them into one category or another. Here’s some examples using an approach I developed around Radian6′s capabilities to find influential individuals.
Restaurant / Dining out Influencers (turned out to be the same 3 people for Sports Influencers):
To learn more, see this post at WebMetricsGuru.com or check out my Social Media Analytics book.