As mobile devices become a major method for authoring and consuming social media, location data is increasingly a part of many posts, tweets, check-ins, and messages. Many Twitter clients, for example, can add the user’s current latitude and longitude to the metadata associated with a tweet. Other systems like Facebook Places, Google Latitude and Foursquare encourage users to declare where they are to the world, often passing the information to other social media sites.
Using this location data in network analysis opens up a range of new opportunities. Instead of a person – to – person social network, location data allows people to be linked to places and, by extension, places can be linked to other places based on the patterns of connection people create when located in a particular place. A convergence of network analysis and Geographic Information Systems in underway. A great example of this can be found in this wonderful video from the BBC which demonstrates the idea by mapping the flow of telephone calls, texts, and data around the UK and the wider world.
Now, NodeXL (v.156) has the first of a series of features that will start to approximate the experience displayed in the video by supporting the import of location data about networks and plotting networks onto maps.
For now, we have started importing latitude and longitude data that Twitter makes available. If you check “Add a Tweet column to the Vertices worksheet” in NodeXL, Data, Import, From Twitter Search Network or From Twitter User Network, the Twitter user’s geographical coordinates will be added to the Vertices worksheet when they are available.
Note that not every tweet has a latitude and longitude, in fact many do not (yet). Further, note that not every latitude and longitude is accurate, many are not.
We need to implement more features for better location data support in a NodeXL workbook, but this is a start. We are interested in exploring geospatial networks and Twitter is a great data source. With this data in place we may look into features that emit KML files for exploration in other packages like Google Earth. A nifty Google Earth/Spreadsheet importer can take small sets (400) of location data points in a spreadsheet and export them to a KML file, something we could implement in the future as well. In addition we may be able to connect directly with services like Bing Maps and Google Maps to display connections between nodes with known locations. Metrics that calculate the distance between nodes seem sensible as well.
Location coordinates are the key to a cornucopia of related data about a place. Given a latitude and longitude it is possible to find the name of the city it is located in, look up data about that location in official records as well as resources like Wikipedia. Income, education, property values, weather, photos, and more can be pulled together from just a simple lat/long. All of these attributes could be used to cluster or illustrate the network visualization.
Third International Symposium on Collaboration, Social Computing, New Media and Networks (SoMNet 2014) Call for Papers and Participation As part of the 2014 International Conference on Collaboration Technologies and Systems (CTS 2014) May 19-23, 2014 The Commons Hotel Minneapolis, Minnesota, USA In Cooperation with ACM, IEEE, and IFIP
RADIO WVXU Cincinnati – Ann Thompson interviews Marc Smith about the recent publication of a report on social media networks in Twitter co-authored with the Pew Internet Research Center. “How millions of tweets boil down to six types of conversations“ http://bit.ly/1eXt6IV #NodeXL #SMRF #Pew #Network
The INSNA Sunbelt social network conference will be held February 18-23 at the TradeWinds Island Resort on the island of St. Pete Beach. There will be NodeXL related talks at the conference. NodeXL: Network Analysis Made Simple Tuesday February 18, 8:00am – 11:00am & 11:30am – 2:30pm Marc Smith, Social Media Research Foundation CITRUS Ballroom […]