The Human brain helps us to make intelligent decisions in everyday life social networks

pre-frontal cortex makes intelligent decisions

The Human brain helps us to unravel the complexity of the social networks.  It can spontaneously access information and help us make intelligent decisions and appropriate responses for acceptable social outcomes.

In a recent research paper published in  ‘Nature – Human behavior’ author Carolyn Parkinson of the University of California talks about how the brain seems to encode the messages we send when meeting familiar people and their position in the social network.  This may not seem like a breakthrough immediately but then the author says this has implications in the way of how we can use this information to understand an individual’s standing in the social network.

In addition, this research can help behavioral studies on how our knowledge of a person’s social standing in a social network can make changes in our attention, empathy, and trust on that person. The brain region where this information is recorded is the higher order pre-frontal cortex and there is a spontaneous access to it.

We interact with many individuals on a daily basis.  Keeping track of our acquaintances and our relationships with others is no mean task.  Sometimes our friends and relatives will have second degree and third degree relationships with their friends and relatives.  It becomes complex as we go on extending the chain.  Now in this complexity, tracking our own relationships and the extended relationships we have with others (not in a sense of self-interest) requires some degree of understanding the relationships.

The question is can the brain in its natural state help us?

Yes, says the research conducted by Carolyn Parkinson of University of California.  Thanks to the Mo Costandi of Scientific American to bring this information to light.

FMRI on 21 MBA Students

Parkinson and her colleagues from Dartmouth College surveyed 275 first year MBA students.  In the survey, the questions where directed specifically towards their social habits.  It included how they preferred mingling with the crowd and with whom they preferred to hang around with and visit their homes.  Their preference in attending social events and so on.

They measured the responses in three different ways.  The first one looked at the ‘degrees of separation‘ from one another.  The second one looked at ‘their closeness to well-connected individuals’ in the social network” and the third ‘the extent of their closeness with aloof individuals’.

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Challenges in analyzing Big data in social networks

big data in social networks

There are social interactions everywhere.  According to the Global Web Index, as of Jan 2016, there are about 3.4 billion internet users in the world.  And within that, there are about 2.3 billion active social media users.  Interactions through social media have become ubiquitous and so is the immense amount of data that is generated through it.

Many popular social networks like Facebook have begun to use this data to know about their users to deliver personalized feed to suit their interests and behavior and the situation is no different in Enterprise social networks as well.

Even today with the enormous amount of data that is generated through social media channels, leaders will have to struggle with the implications of big data.  Analyzing and gleaning information from the data will become key factor for competition as well as rise in productivity, innovation and increase in consumer surplus says the Mckinsey in their report  “Big data: The next frontier for innovation, competition, and productivity”.

Many people from senior leadership teams to the people in the technology world talk about ‘Big Data’.  Big data is not a buzzword for smarter data analysis to gain insight.  Therefore, what exactly is Big data, what does is it mean for us and how can we use the insights gained from such analysis in the realm of social networks.  The data analysis and insights gained is significantly different from what managers might generate from regular analytics.

Big data in social media

Big data is all the voluminous and unstructured data from a wide ranging sources in the form of click stream data from  websites, social media data like ‘Likes’, Tweets and ‘Blog posts’ etc. and from video entertainment as well.  Just to give you an idea, Google processes about 24 petabytes of data and not all of this in rows and columns.  Sometimes organizations also take into account the real time information as it occurs in radio frequency identification systems and make changes as they happen.

The consumers as well as working professionals in the organizations have begun to realize the potential value and the intelligence that can be derived from the vast amount of data that is generated through social media conversations.

Big data applications largely depend on their ability to analyze this large and unstructured data and handle the scale of the geometric growth of the social networks.  Social networks generate conversations and there is context attached to these conversations.  It is this context to information from various expert users is what makes knowledge sharing through social media tools so invaluable.  Finding specific information in this vast sea of billions of conversational messages is no easy task.  Big data in social networks together with social analytics need to go hand in hand in finding out the specific information we need.

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