Mining the Super Bowl with Geofeedia, NetBase and Salesforce Radian6

Posted on February 4th 2014

Mining the Super Bowl with Geofeedia, NetBase and Salesforce Radian6

I meant to write a longer post and watch the Super Bowl on regular Cable TV, but ended up watching in on Geofeedia; I set up a Geofeed (from Geofeedia) 4 days ago along with a NetBase profile to use with my viral emotion filters that I tuned around the work of Unruly and Social Videos (see my Slideshare presentation around Viral Sharing). I also set up a Radian6 topic profile around the Superbowl using the same set of keywords I picked up from Geofeedia.

Source: – Superbowl – 1/30/14 – early morning 2/3/14

I was pretty happy with what I was getting from Geofeedia and it was clearly coming right from the Super Bowl only. But just how much of the chatter in social media was actually “at the Superbowl”?  I used Radian6 to capture the total number of Twitter and Instagram posts and compared them with the mostly Twitter and Instagram posts I got from Geofeedia.

Radian6 (total Instagram + Twitter) and total Geofeedia Super Bowl using keywords and hashtags pulled from Geofeedia

It looks like a little less than 1% of the Instagram and Twitter chatter was actually at the Super Bowl - but I don’t think the Radian6 numbers are that trustworthy (since I doubt they could be replicated) – but I’ll go with them for this analysis.  Remember – I used the same hashtags and keywords as I was picking up from Geofeedia - if someone else created this topic profile in Radian6 they would probably use a slightly different set of terms and come up with different numbers than I have.

Geofeedia picked up 5705 users who engaged in social media using Twitter or Instagram (there were  Flickr/YouTube/Picassa users, but not very many) over the 3 days, more or less, I was monitoring the Superbowl.

Assuming 86,500 tickets were sold (total seat capacity) at the MetLife Stadium between 6% and 7% of the people at the Super Bowl last night were on one of the forms of social media I am tracking using Geofeedia (we’re probably closer to 7% than 6%) – that’s a pretty decent number, and remember Sports events draw out a lot of social media (as does Fashion, Food and Music events).

Top Twitter, Instagram and Piccasa accounts at the Superbowl

Top keywords used at the Superbowl – Geofeedia

Depending on how you combine platforms you can get some pretty interesting results – we already know what Geofeedia can do and lets do our best to replicate, to the extent we can, the Geo-location aspects that Geofeedia has down pat, and combine them with NetBase viral filters aka Unruly and the Science of Sharing work that is part of my new Rutgers course that is launching this month on Creating Viral Media and measuring it using Big Data.


NetBase Dashboard (filters) created around the Science of Sharing and Unruly ideas about Social Sharing

- This was done a few months ago for the NetBase Webinar  and the image above canme from Viral Marketing, The Science of Sharing by Karen Nelson-Field

I use NetBase’s unique metric called “Passion Intensity” which fits most closely with the work of Karen Nelson-Field as outlined in her book, Viral Marketing, The Science of Sharing, published by Oxford University Press late last year.  While strong emotions can predict viral sharing, the same filters can be reversed and used to find content that is a certain emotion.

My “viral” analysis done with NetBase and based on Unruly and the Science of Sharing – this was done around 11 viral videos last October from Unruly Viral Video Chart.


Note: Passion Intensity it the predominance of what NetBase calls “strong emotions” to all emotions detected via text analytics.

Take “Anger” – it’s a strong negative emotion according to Karen Nelson-Field but the Passion Intensity is only 23.

 Disgust is also a strong negative emotion, but a lot of people felt it last night at the Superbowl, and they felt it passionately.

NetBase – Disgust filter – very strong emotions 46,000 posts about Disgust and over 15,200 of them were strongly negative

 Exhilaration is a strong positive emotion and it was strongly expressed last night at the Super Bowl – but it seemed that many were saying the Puppy Bowl as more exciting – ha!

There’s a lot here to explore, but we explore it through my Rutgers course which is launching this month – and besides, I’m totally exhausted from being up all day and it being almost 4:30 AM Monday morning.


Marshall Sponder

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