The filter bubble is changing our judgment.

Every day we are receiving the information from the news website, social media, and Google. Usually, we do not have a particular topic and to search the related information to read. We pick up our smartphones and click on our favorite website or click on what looks interesting. What else, we look at the Twitter or WeChat movement to see what your friends are sharing.

But did you know there is a complicated algorithmic system behind your screen? We call it Filter Bubble.

Hallinan and Striphas (2014) define algorithmic as the ‘use of computational processes to sort, classify, and hierarchize people, places, objects, and ideas, and also the habits of thought, conduct, and expression that arise about those processes’

To put it simply, based on algorithmic, the media platform can guess what information an audience would like to read. For example, when you always search shoes on eBay. Then, when you were surfing in the Instagram, some advertisement of shoes will appear. 

That does not sound bad, right?

But let’s dig deeper into this phenomenon. Many media platforms offer personalized content based on our characteristics. The result is all the opinion of an article and post is same with us. We will think that all over the world have the same opinion, and we forget that other perspectives exist.

For example, in 2016 U.S. persistent election, Donald Trump credited his social networking accounts for leading him to victory. When all your friends around you who are supporting Donald Trump. The result is most information you get from media platforms is about Donald Trump’s political view. 

One of the great problems with filters bubble is audience tendency to consider that all we see is real.  As the words by Carah (2016), filters bubble is an open-ended and responsive phenomenon. The more audience engages with the brand, the more the brand can use algorithms to attune itself to their identities.

Reference:

Carah, N. (2016). Algorithmic brands: A decade of brand experiments with mobile and social media. New Media & Society, 19(3), pp.384-400. 

Hallinan, B. and Striphas, T. (2014). Recommended for you: The Netflix Prize and the production of algorithmic culture. New Media & Society, 18(1), pp.117-137.