Social Media Echo Chambers: The Bubbles We Create – Part 1


The Realisation

It all started with one of our many discussions at the office. I must explain that our data lab promotes informal discussions about tech and our social responsibility as data professionals. Because of this I have found myself, pausing and thinking about the many things we still need to address and improve. On this day This we landed on day the topic was “Echo Chambers” and how it was affecting society.

That night I went on a “curiosity research”. I wondered “Why don’t more people know what an Echo Chamber is? How do they know if they are in one?”. A non-technical friend said to me; “You work in that world, of course you understand how algorithms are amplifying echo chambers. How many of the people that it effects know or get this information? Tech people have their own world”.

“But the tech community does a lot on awareness, we really try to educate people” was my response.  I wanted to defend my community; my biases made themselves visible. “Where do you guys do that on your own preferred platforms and in your own language, right?” …  he was right! Even when we are trying to help, we are doing it our way and speaking our own language.  Then it hit me … like a ton of bricks …

Am I… In an echo chamber?

So, with an open mind and fresh perspective, I took a new look at Social Media Echo Chambes.

The Bubbles We Create

Wikipedia defines it as an echo chamber as refers to beliefs that are amplified or reinforced when reparative or similar content is communicated inside a closed environment and insulated from opposed belief. By participating in an echo chamber, people are able to seek out information that reinforces their existing views without encountering opposing views. The result is an unintended exercise in confirmation bias. Echo chambers may increase social and political polarization and extremism. In other words, segregation of communities.

According to Wikipedia the term is a metaphor based on an acoustic echo chamber, in which sounds reverberate in a hollow enclosure. Another emerging term for this is echoing and homogenizing effect within social media communities on the internet is cultural tribalism. To really understand the phenomenon, we should look at the very beginning of the internet itself.

How did we get there?

The origins of the Internet date back to the development of packet switching and research commissioned by the United States Department of Defense in the 1960s to enable time-sharing of computers. The pioneering network, the ARPANET, initially served as a backbone for interconnection of regional academic and military networks in the 1970s. The funding of the National Science Foundation Network as a new backbone in the 1980s, as well as private funding for other commercial extensions, led to worldwide participation in the development of new networking technologies, and the merger of many networks. The linking of commercial networks and enterprises by the early 1990s marked the beginning of the transition to the modern Internet and generated a sustained exponential growth as generations of institutional, personal, and mobile computers were connected to the network.

We could look at the internet as having 3 versions:

Web 1.0

In the 1980s the internet was mostly used as a repository of information, think of it like a giant library. But the information stream was 1 sided, users could only get information and not much else.

Web 2.0

In the late 1990s the internet made a significant evolution, user generated content. The internet had expanded to a 2-way information street, users could interact with the website. Website like WordPress, and who can forget MySpace. Brings back memories, right?

Web 3.0

Started in the late 2005 and is the point we are at now. The evolution is visible and continues. The connection is triangular shaped now. Meaning, not only can the users interact with the website and create their own content, but they can also interact with each other. Basically, social media as we know it today with platforms such as Facebook, Twitter and Instagram, and at the beginning of this era: Youtube (yes, Youtube has been around for 15 years)!

The internet was a place buzzing with creativity with the goal to do good and be better. But somewhere it feels like it has lost some of its sparkle.

How Algorithms Are Creating Echo Chambers

Let me start with saying that social media did not create this social phenomenon we are dealing with today. At it’sits roots you will find social, individual, educational and psychological aspects which we will dive into in part 2 and 3. For now’s let look at the technological aspect only.

I’ll use Facebook as an example not because I dislike Mark ZuckerbergZuckerberg, or I want to bash Facebook but because they are one of the biggest platforms with 2.7 billion users. By no means is Facebook the only Social Media Platform guilty of increasing echo chamber on their platform. Many others share the same guilt think about Twitter, Google and yes even LinkedIn who are also guilty.

Social media algorithms are a way of sorting posts in a users’ feed based on relevancy instead of publish time and feed personalized content back to the user. The first algorithm Facebook used was EdgeRank which sorted based on 3 things:

  • Timeliness – How recent is the post
  • Engagement – How many likes, comments and share a post accumulated
  • History – Previous interactions

Facebook stopped using this algorithm in 2011. Which algorithm they have replaced it with is not known. The models are considered trade secrets, so Facebook is very vague when explaining how it actually works.

By default, social media algorithms take the reins of determining which content to deliver to you based on your behavior. Facebook might put posts from your closest friends and family front-and-center in your feed because those are the accounts you interact with most often.

You have all received recommendation on videos to watch on YouTube, right? This is again based on your individual behavior, digging into what you’ve watched in the past and what users with similar “persona” like you are watching. Elements such as categories, #tags and keywords also factor into recommended content on any given network.

Algorithms are constantly evolving because the algorithms keep trying to be more accurate and by engineers refining them, in an attempting to better predict your future needs and provide the best user experience possible. As a result, marketers have to consistently adapt to them. This means companies and content creators are consistently experimenting with content and changing up marketing strategies.

Without social media algorithms, sorting through all of this content on an account-by-account basis would be impossible. Especially for users following hundreds or thousands of accounts on a network, so algorithms do the legwork of delivering what you “want” and weeding out content that’s deemed irrelevant or low-quality.

In theory, that is. We will talk more about this later on.

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