January 15, 2021 Author: Matthew Renze

In my previous article in this two-part series, we discussed information bubbles. We discussed what they are, how you get stuck in them, how they can affect you, and why they can be dangerous.

In this article, we’ll discuss five types of information bubbles that exist and how to identify, escape, and avoid each of them in the future.

 

Filter Bubbles

A filter bubble is an information bubble that exists in your internet search results. You find them on search engines like Google, Bing, Yahoo, Baidu, and many other search engines.

Search-filter bubbles are caused by search algorithms learning your preferences over time using machine learning. Once they’ve learned your preferences, they filter your search results to mirror your personal biases, based on your past search history, location, demographics, and the previous search results you’ve clicked on.

However, there are also government-imposed search filters that selectively restrict the information that you can access from within your country. For example, if you perform a search for a controversial topic in one country you may get vastly different results from the same search in a different country.

To identify if you’re in a search-filter bubble, perform a search for a controversial topic using your regular search engine. Next, perform the same search using a different search engine and/or with an in-private browser and a Virtual Private Network (VPN) outside of your country. Note: please make sure this is legal in your country first.

Next, compare the top search results. Do you see any political bias in your search results vs. the alternative search results? Are search results skewed towards less reliable sources of information vs. the alternative results? Are important factual sources being completely omitted from your results?

To avoid search-filter bubbles, perform controversial searches using a search engine that does not personalize your results (e.g. DuckDuckGo). I also recommend using a VPN with an in-private browser (or an anonymous browser) to hide your identity. You can also clear your search history to avoid some of the personalization too.

 

Recommendation Bubbles

A recommendation bubble is an information bubble that exists in the content being recommended to you by content-recommendation engines. You find them on media sites like YouTube, Netflix, Amazon, Disney+, etc.

Recommendation bubbles are caused by content-recommendation engines learning your preferences for content using machine learning. Once they’ve learned your preferences, they present you with new content recommendations that mirror your existing biases based on the content you’ve viewed in the past.

To identify if you’re in a recommendation bubble, create a new account using an in-private browser and a VPN. Do the content recommendations match your existing account’s recommendations? Are your recommendations biased or skewed towards a specific political ideology or worldview?

As an added bonus, feel free to test how quickly a recommendation bubble develops by clicking on a few controversial topics in a row and watching new content recommendations. It’s surprisingly fast!

To escape and avoid content-recommendation bubbles, I recommend using an in-private browser and a VPN when researching or viewing any political or controversial content. If the website requires a login then use a separate account for researching or viewing this controversial content.

I also recommend selectively removing controversial content from your viewing history or clearing your entire content history, if necessary. Also, be sure to choose sites that allow you to see and manage how recommendations are being generated.

 

News Bubbles

A news bubble is an information bubble that exists in the news sources you consume. You find them in newspapers, cable news, internet news, and via your news feed.

News bubbles are caused when we choose only news sources that confirm their own biases and worldview. Once we find a news source that mirrors our beliefs, we have a tendency to consume more of that news and disregard other news sources. This tendency is called a news selection bias.

To identify if you’re in a news bubble, simply scan the headlines from a few news sources that are known to be reliable and unbiased. For example, the Associated Press and Reuters. Do their headlines match the headlines in your news sources, or do they look like two completely different worlds?

To avoid a news bubble, I recommend choosing only news sources that are known to be factually reliable and politically unbiased. You can determine the reliability and bias of news sources using an independent verification site. For example, Media Bias Fact Check and Ad Fontes Media Bias Chart.

If you need help determining how to choose reliable sources of news, please feel free to check out my previous article Six Steps to Stop Fake News.

 

Social-Media Bubbles

A social-media bubble is an information bubble that exists in your network of contacts on social media. You find them on Facebook, Twitter, LinkedIn, Instagram, Pinterest, TikTok, and other forms of social media.

Social-media bubbles are created in two ways: First, they are created by us choosing who we follow or unfollow on social media. Second, they are created by others choosing to follow or unfollow us.

It can happen very quickly; you post something politically controversial and a subset of your followers who disagree with your view will (silently) unsubscribe from your posts. In addition, when someone else posts something that you disagree with, you may unsubscribe or ignore their future posts as well.

Unfortunately, it’s very hard to verify if you’re in a social-media bubble. These websites don’t allow us to see who has unsubscribed from our news feed or explain why our followers have unfollowed us. However, if you notice that opposing political voices have gone silent, you’ve likely fallen into one.

To avoid social-media bubbles, don’t unfollow people simply because they disagree with your views. In addition, avoid posting information (even if it’s factually correct) in ways that will cause others to unfollow you. If seeing dissenting viewpoints stress you out, consider leaving social media altogether.

 

Social Bubbles

A social bubble is an information bubble that exists in the network of people you directly interact with. You find them when chatting via voice, video, text, email, and face-to-face conversations.

Social bubbles are created when we self-select the people that we interact with and engage with in conversation. We tend to choose our conversation partners based on similar political views and ideologies. Eventually, this can lead to an echo chamber where we’re only hearing one point of view.

To identify if you’re in a social bubble, ask a few people outside of your social circle for their honest opinions on a controversial topic. Do not argue, debate them, or inject your own views. Just listen to them and thank them for their honesty. Do their views agree with the views of your circle of friends?

To avoid social bubbles, make friends with people who are different from you and your circle of friends. Choose people with different cultures, religions, skin color, sexual orientation, etc. Intentionally choose to have a diverse and inclusive group of friends to enrich you with a variety of differing ideas.

Also, learn how to have conversations about controversial topics without confrontation. You need to be able to ask people about their opinions, listen to their thoughts, and respond without being defensive. This is a skill you must learn with lots of practice; however, I know a great book to help you get started.

 

If you’re interested in learning more about information bubbles on the internet and social media, I recommend Eli Pariser’s TED Talk and the documentary The Social Dilemma on Netflix. It may very well be the most important documentary of the decade.

 

If you’d like to learn more about the technologies behind information bubbles and how you can use them responsibly and ethically, please check out my online courses on AI and ML.

Share this Article