Who is who in the net: Computing and modelling people digital footprint

Material

  • Code examples to harvest data from your social networks

Description

In the era of social networks, every user can have an associated digital footprint that integrates the history of her digital life. This footprint contains information that even the owner might not know about her preferences, her direct and indirect connections, the communities she is related to, the way her discourse and interests make her close to people that she might not know. A person with an active activity in social networks has different information and connections to in each of them.

Me on Facebook is not the same me in snapchat, in foursquare, in linked in or Instagram. I might belong to different communities (e.g., professional, familiar, interests) and expose different interests towards several topics. Having a global and integrated view of one’s foot print can have political, personal and commercial interests. It can also be possible to discover possible connections of different kinds with other people and communities and make my digital and non-digital experience better.

  • Compute an individual digital footprint graph by correlating and integrating data from the social graphs from their different social networks.
  • Given a digital footprint graphs discover possible relations with other individuals using two relations discovery techniques. For example, starting from a portion of the digital footprint graphs consisting of all individuals present in a community of contacts, this process “completes” the graph with new discovered relations tagged with precision probabilities.