A couple of weeks ago we were pleased to spend some time with the folks from UNICEF, analyzing and discussing their #SahelNow campaign. The campaign is focused on drawing attention towards the food crisis unfolding in the Sahel region in West and Central Africa. The campaign’s goal is to rush food, nutrition and other emergency relief to help children in the region. There is an urgent need for help from the public, and #SahelNow is an attempt to alert the world about this looming crisis. SocialFlow supports the effort to enlist people around the world to help to sound the alarm.
The campaign has seen a substantial rise in references, including participation from a number of major celebrities. The SocialFlow research team helped UNICEF analyze and understand hashtag usage across Twitter by looking at a few different aspects of the data:
- Time Series Data: by mapping out levels of hashtag references we could identify prominent points in time where the conversation was spiking out of the ordinary
- Phrase Co-occurence: we generated a network graph view of all the related concepts referenced in Tweets along with the hashtag (concepts = phrases, other hashtags, users)
- Friendship Graph: we extracted the underlying network of relationships amongst those users who referenced the #SahelNow hashtag, in effect identifying dense clusters of users who were actively promoting the cause in their region.
Here’s a video highlighting some of the data manipulation we ran using gephi:
And attached below are a number of screenshots we took during the analysis:
Image 3: Relationship graph showing connections between users who posted to the hashtag. Note the dense clusters that emerge highlighting different regional and topical communities that reference the campaign.
Questions? Feel free to ping me on Twitter | @gilgul