A system for extracting structural information from Social Network accounts

The social network phenomenon involves hundreds of millions of people every day. This enormous volume of activity results in a huge source of information that can be valuable in many fields, for both research and application purposes. The relevance of this information strongly depends on the evolution occurring in the social Web, in which interaction among different social networks and their cross‐relationships are becoming progressively more important. This, in fact, represents the basis of an emergent scenario called Social Internetworking Scenario. However, efficiently accessing and fruitfully querying this huge information source is not easy, because no tool to support applications needing a massive utilization of cross‐social‐network data exists. In this paper, we fill this gap by proposing Social Network Account Knowledge Extractor (SNAKE), a system supporting the extraction of structural data from a social network account. SNAKE is implemented in such a way as to be easily integrated in any social‐network‐based application. To show the practical relevance of our proposal, we present our experience gained in three possible real‐life applications strongly relying on information provided by SNAKE.