Location implies specificity in the physical world. If you are here, you cannot be there. To mark your position, we can attach spatial coordinates (like latitude and longitude).
In Reality Mining as well as in Barabasi’s research on human mobility, data is derived from human mobility patterns in physical space. Mobile phone tower data is used as a proxy for determining individual movements. For each user, a mobility network is constructed, where the nodes are specific locations and the paths between the nodes represent transitions. The mobility network is used to predict an individual’s future movements to considerable accuracy.
To translate this thinking to the online world, we must first give meaning to our notion of “location.”
The obvious solution may be to consider using web pages. In Sergei Brin and Larry Page’s initial whitepaper, a probabilistic model of the web graph was built to determine the importance of web pages for ranking search results in Google. The individual nodes are web pages and the paths between the pages represented web links. In their model, location is defined as particular web pages (web surfers transition between web pages). This is not ideal for our model for several reasons:
- Web pages intrinsically represent content, and content represents ideas. What we would like to build is an understanding of the associations between ideas or concepts. While the web certainly contains an implicit relational structure between topical areas, there is a finer semantic granularity to be deduced.
- The web is a messy place. Web link structure may not always be an indicator of importance, nor do two pages dealing with the same subject represent two distinct locations. Content and concepts are the intrinsic “atoms” which we would like to model and understand.
At Sociocast, we process immense amounts of structured and unstructured content (what we call user observations) and apply natural language processing techniques to extract their meaning. Once we deduce the particular contexts or topics within an observation, we can determine a user’s location by inserting them into what we call Context Space.
We look to understand the associations between concepts, by observing how people transition between them (sequentially).
A simple but powerful idea.
(Flickr)