Role of Social Media in “Polarization” of communities

Anurag Srivastav
3 min readDec 1, 2020

Social media has been a great influencer. With the advent of mobile data, content is now reaching to millions of people directly one-to-one. No one comes between the content generator and content consumer to moderate the narrative and view points. Facts and Fiction, both travel at the same speed and reach the content consumers without any hurdles.

In contemporary world, everyone has right to access the information. This also means that those countries, organizations and communities promote this idea of “free access” to information are considered truly “independent” and “democratic” and hold a high moral ground. With the advent of social media this principle is now being met. The governments who try to control the social media are branded as “authoritarian”. This unfettered access to content, while “good, in principle”, does assume that the consumer is wise enough to be able to understand the big picture and react accordingly.

It is known fact that each individual interprets the same information in a different way, based on his / her mindset, situation at hand, location, time, etc. Various factors influence the outcome from different persons for same input. More so, same person may react differently to same information at a different time. This would lead to a simple conclusion that unfettered access to information can lead to mis-interpretations.

Social media, with its boon of information exchange, has become King Midas (from Greek mythology). While it makes gold (money) from everything it touches, it is also creating a social divide with deeper fault lines and differences. While Social Media can create opinions and thus fetch votes for parties, it can also create irreconcilable differences in the supporters with its propaganda war. There can be false narratives as well as twisted facts leading to various incidents seen across the globe. If the social media can unite, group and bond people, it also has the power to create rifts and widen them with time.

Are Social Media companies at fault for this? The usual excuse is — content generation and consumption is by and for the subscribers. We (the companies) themselves do not generate or even promote a any point of view. Thus, the responsibility lies on the subscribers. Further, stopping the content from being uploaded or banning the content is an encroachment on the rights of subscribers as well as this makes them take sides. Therefore, unfettered and unedited information should not be blocked. This is right approach.

The issue actually emanates from a different place though. On a closer analysis, one may find that it is actually the content delivery system which creates the problem at first place. In the name of “Recommendation Engine” and “tuning the content to the preferences of a subscriber”, the systems continue to feed “a specific side” of the picture. Thus, if a subscriber is interested in a specific point of view, the system automatically makes sure that same perspective is brought to them repeatedly and continuously. The “other side” of the story is rarely presented. This leads to imbalance in the approach and thought process of individuals.

Further, when it comes to communities, such viewpoints or twisted stories keep showing up on individual’s personal communication system. This leads to subconscious “brainwashing” and thus firming up of a narrative. The outcome is — deeper divide in the society. This divide causes disproportional responses from each side and widening rifts.

So, what can be done? Can Social Media companies change their recommendation system to serve a balanced viewpoint to a subscriber? Can we tune the systems to serve a bouquet of content with sum-total balanced view point? There has to be a difference between a recommendation engine on an e-commerce website and news / media / social media website. This is where Social Media can play bigger role. Generate moderate societies and balanced perspectives.

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Anurag Srivastav

Telecom Expert with avid interest in Big Data Analytics