Analysis of the growth of Wikipedia quantifies strength of correlation and cultural similarities/differences. Facebook data reveals the opinions, choices and tastes (and therefore the community structure) of people. Similarly query logs anticipate stock-trading volumes.
Note that the issue of actual prediction has been recently challenged ). įor example, the analysis of query logs from search engines and analysis of Twitter anticipates the spreading of the flu – (simple checks of this phenomenon can be tested with the Google service Flutrends - where data are aggregated on a weekly timescale.
Irrespective of the technical differences and their usage, the various social networks like Facebook, Twitter and Google+, and search engines like Google and Bing, have relevant information stored in their stream of data. Social science, statistical physics, computer science and network theory methods are being used by scientists to deal with these big data analyses. Not surprisingly then, many studies from different field perspectives are trying to understand how to deal with this new social phenomenon and how to extract information from the continuously growing repositories. Differently from other societal changes in the past, we are now able to track down the onset and evolution of this revolution by inspecting the big-data records of social media.
It is fair to say that the present widespread access to the Internet is changing the way in which people interact, exchange information and communicate, resulting in a profound transformation of our society and our economy. The recent growth of social networks has created various communities of millions of users in constant evolution.
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