Tuesday, April 4, 2017

Posted by beni in , , , , , , | April 04, 2017

A Facebook user profile through Big Data


A research by a computational knowledge engine shows how people meet and how their life works by analyzing friend and relationship status in Facebook. The research was volunteered by more than a million Facebook users on Wolframs site. Wolfram research analyzes each and every activity of a user and used it to generate reports for the activity of users in United States.
 Reports can be generated for each Facebook user and these reports are amazing. Word cloud, relationship status of friends, distribution of friends ages, friend network and many other fascinating reports could be generated. Friend clusters are made and friends are classified into social insiders (a friend who share a large number of friends), social outsiders(a friend who shares at most one friend), top social connectors(a friend who connects together group of friends who are otherwise disconnected), top social neighbors(a friend with small number of out-of-network friends - friends of theirs that we dont know) and top social gateways (a friend with large number of out-of-network friends). Basically terms are coined by using graph theory.
These are some of the screenshots from my (Robin Muthukumar) report

My activity in Facebook

Friends network

Color coded friends network

Each user can get his/her own report by using this link http://www.wolframalpha.com/facebook/
Data like these were analyzed and compared to the United States census data and both were found to be identical. This kind of research help the Government to monitor peoples mindset and pass bills or amend laws accordingly. This kind of research helps politicians to gather their votes. 

Reference: http://bits.blogs.nytimes.com/2013/04/25/looking-at-facebooks-friend-and-relationship-status-through-big-data/

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