Ep 7: Grey Swans

So far in our podcast, we’ve been encouraging people to break out of their bubble through discourse, good questions, and a careful awareness of bias in media and how we consume news. But we’d be remiss to not point out the obvious bias we often see in ourselves when we’re seeking out information.

This week, we tackle the idea of “confirmation bias” at all its levels– in how we ask questions and sometimes skew the responses we get, in how we think about what is “given” and what we can question, and even how searching for positivity can result in being happier.

Listen to “Grey Swans” (and how we adapted Nassim Taleb’s concept of Black Swans for our title)  :

You can download the episode here.

And here are links for some of the things we mentioned:

And as always, let us know if you have feedback, comments or questions by posting here, on our Facebook page or at breakingthebubblepodcast@gmail.com.

Ep 6: Posting and Low-Blows and Trolls, Oh My!

We see them more and more these days. And not just because people are posting more often, but because their reach is getting amplified with social media shares and “viral” statuses. These cathartic, explanatory, uplifting, or sometimes ranting social media posts are making their rounds on newsfeeds around the world.

This week, we take a look at some statuses we’ve seen in our feeds to better understand what pushes someone to share personal details or stories on a platform that is very public.

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In the process, we found that posting things to social media sometimes reinforces your echo chamber, but it can also break through to bipartisan discussion or invite the ever-feared “Internet Trolls” (dun dun dunnnnn).

Listen to this week’s episode on “Posting and Low-Blows and Trolls, Oh My!” :

You can download the episode here:

If you have any questions for our featured guests, leave a comment here or email us at breakingthebubblepodcast@gmail.com.

Ep 4: Reverse Engineering Statistics

RevEng Stats-03

Yes, we realise this title sounds just a tad nerdy. What can we do? We’re journalists who like numbers (gasp!!!!).

But really. Numbers can lie just like people do. And it’s important to be able to recognise when a specific stat has been manipulated to tell you just one side of a story. We hope today’s episode will help with that. Let us know what you think!!


Download the episode here.

As you heard, we mentioned a lot of links in that episode. You can check them out here:

And let us know if you find any other case studies that are related to this topic! We’re always looking for more material.

Ep 3: The Role of Humo(u)r

We’ve both been big fans of The Daily Show for years, and back in college, we often found ourselves relying on Jon Stewart and Stephen Colbert for more candid, unfiltered versions of the news.

But that in itself is a problem. And as the political climate around the world has become more charged, it seems ‘unfiltered’ is a thing of the past.

All media today seems to have an opinion. And that combined with the stress of gaining viewership means TV news channels are getting more and more sensationalised, often following the path of comedy shows and late night programming.

Does that simply make the news more interesting? Or is this a dangerous trend?

This episode was super fun to research, discuss and edit because of all the examples we’ve sprinkled throughout. Check it out and let us know what you think!!


Download the episode here.

Here are some of the links to the video clips we mentioned:

The Daily Show: “Trump is an Old Man”

The Daily Show: “The Israel World”

AJ Plus: Trump’s Handshake

AJ Plus: “Is Donald Trump a Fascist?”

 

We also mentioned a specific back and forth between an O’Reilly Factor segment and a response from the Daily Show, as well as what we thought would be comparable precedent. 

O’Reilly Factor:

Daily Show Response:

Previous Daily Show segment in the same vein:

 

Coming soon:

Reverse Engineering Stats! We got a lot of positive feedback on the How to Question Everything episode so we decided we’d take it one step further… How to question the statistics and numbers you see cited in articles. More specifically, how to reverse engineer them to see if you’re being misled.