Been thinking about all the Geo-location data I’m collecting in Radian6 around 4SQ check-ins. Truth be told, I started tracking check-ins to see if I could do, more or less myself, what Dave Kerpen had a team of 6 staff members doing with his Likeable Social Media book (his team attempted to put a hint to download a free chapter of his book, or buy it, into every bookstore in the country).
Not sure what the results of that attempt to use a social media clan to harness popularity did – but his book ranks very well on Amazon, esp in the Web Marketing Category.
But I quickly gave up on the idea, not because I could not do it, it was simply my approach is different, I’m an analyst, not a marketer. I can get others to do marketing for me, but fundamentally, my work is with revealing truth, not spreading it. I let the quality of my content take care of itself – too busy creating content to worry about putting hints all over the internet – but, I admit, Dave Kerpen, in attempting to use Foursquare the way he did, developed a service with is probably useful to other book marketers as a product, in and of itself. Still, that’s not where I wish to go (or really can go, to be honest).
Before you is all the check-ins from Foursquare over the last 3 months that have been capture, mostly on Twitter. Based on my numbers (any mention that has “4sq.com” in it) there are roughly 7 million check-ins that take place a month, over the last 3 months.
For some reason, possibly due to changes I made in the topic profile, the latest volumes aren’t up to what they were for most of the 3 months, but I think you can see check-ins tend to peak during weekends, particularly Saturdays (not unexpected).
My queries are based on what I’m finding in the data, but admit they could be expanded – I don’t claim they are as good and clean as they could be – and therefore, these numbers presented in the charts below, really depend on the queries, which will differ from analyst to analyst – there is simply no standard yet, that exists on query writing. It’s a young field yet, Social Analytics – as I pointed out in my book.
Still, there’s not much you can do with the information unless your willing to segment it into categories, and even subcategories. In fact, the more categorization on can do on data, providing your categorizations are useful and fit the use-case your after (and I’ll have to explain that some other time) your data will become much more actionable. Without the categorization, its almost next to useless.
Doing this wasn’t that hard, and generally speaking, working with Geo-located data has some advantages in that context and structure, to some extent, is provided by the check-in, itself. Looking at the River of News for each category gave me, more or less, satisfactory results. Had I had another 20-30 hours I could have whittled away and categorized the 2/3 of check-ins that are left, but I didn’t bother to do that.
A couple of observations – since we know the total number of check-ins was 19,307,702 (19.3 million) then
- 4% of all check-ins were about Mayors (awarded/ousted, etc).
- 5% of all check-ins were about Badge activity (awarded/achieved)
- 2% of all check-ins was about arriving @home and relaxing (finally)
- 2% of all check-ins was about Sports Activity (of some sort)
- about 1/2 of a percent of check-ins was about being @work (but more could be done here with a fuller set of linguisticVariants - see chapter 3 of Social Media Analytics)
- Restaurant/food/dining out has the most check-in activity at 5% of all check-ins.