In 2013, I spoke about the theory of “social media bubbles”. This concept was unheard of three to five years ago. However, the internet and social media such as Google, Facebook and Twitter have evolved and are now powered by sophisticated machine learning algorithms that work endlessly to predict user behaviour based on a set of online activities, mouse clicks and keywords. Some of the common machine learning algorithms used are Market-Basket Analysis, Naive Bayes Classifier and many other derivatives.
A good example to illustrate this further is Google. Google has successfully championed this by crawling the content of your emails, keyword search, internet websites which you go to and compile them into a categories that define your “behaviour” or “perceived interests”. The next time you search a keyword through Google, chances are the search results will be matched closely to your historical or perceived interests (eg.: cars, fashion, entertainment, travel, political parties, etc.). Essentially, this will create a phenomenon known as confirmation bias.
In the same manner, similar algorithms are applied in Facebook too, where users are shown Facebook posts by people in your Friends list, or those whom you have interacted with (by clicking like or posting comments on their profiles) in the last 24 – 48 hours. This has largely impacted how we view things on Facebook as the First-In-First-Out (FIFO) timeline approach has been replaced completely by a set of algorithms. Consequently, this has the potential to increase the likelihood of a confirmation bias, while at the same time users are getting more trapped in their own “social media bubbles”.
The creation of social media bubbles based on common interests, psychographics, demographics and other parameters have influenced how we view the world.
Our research using social data analytics (with a sample size of 192,000 social media posts across 64,000 unique users) based on two distinct topics shows precisely how social media bubbles are formed and interact with one another. Each bubble in the chart represent a unique user. The colours (red, grey and green) represent the net sentiment score of each user.
Placing those bubbles together in a John Venn diagram, one can see that groups of people who were actively engaged in a Topic #2 were not entirely engaged in Topic #1 due to different interests, demographics and psychographics. The slight overlap in the Venn diagram shows there were people who are actively talking about both topics.
This observation supports the hypothesis of how we, as the netizens in the digital world, are constantly trapped in our own social media bubbles. This in turn, has an adverse effect on confirmation bias and how we perceive world events, product launches and other events that are happening around us.