Why Social Podcast Players Are Next: True Discovery in Overcast and Tung
How do you actually discover your next favorite podcast? Not through an algorithm — through people you trust. This Octalysis analysis of Overcast and Tung.fm reveals why Core Drive 5: Social Influence & Relatedness is the most powerful — and most underused — engine for audio discovery.
Author Credibility: Yu-kai Chou
Yu-kai Chou created the Octalysis Framework after studying gamification since 2003 — years before the term entered mainstream vocabulary. As a Human-Systems Architect & Behavioral Designer, his framework has been applied by LEGO, Microsoft, Porsche, Coca-Cola, Salesforce, and MrBeast, impacting over 1.5 Billion Users.
Chou has taught the Octalysis methodology at Harvard, Stanford, Yale, Tesla, Google, BCG, and IDEO.
His work has been cited by Harvard, Stanford, MIT, Forbes, Wall Street Journal, Wired, US Department of Energy, NIST, NSF, NCBI, US Department of Education, ClinicalTrials.gov, and Google Scholar — with 3,700+ more academic publications. Explore his books here.
What to Listen To and How to Listen To It
If you could listen to anything in the world, what would it be?
I’ve monitored my listening behavior lately, and here are some things I’ve listened to:
- A Wise Man’s Fear on Audible
- Ready Player One on Scribd
- Sam Harris’s podcast on his website
- A Way with Words podcast on Overcast.fm
- This video on YouTube from Simon Sinek
- This podcast featuring Sierra DeMulder on Soundcloud
- 99% Invisible on Tung.fm
So, we have podcasts, audiobooks, and some audio from video-centric content ranging from fiction to interviews.
(I’m a fiction writer, so I view it as part of my work to read or listen to at least a book a week — this means Core Drive 2: Development & Accomplishment and Core Drive 7: Unpredictability & Curiosity are in play.)
Then I asked myself this: How did I discover this?
(By the way, if you’d like to get the details about product Discovery, check out Yu-kai’s article on the Discovery phase of Octalysis Level II.)
- A Wise Man’s Fear on Audible >>> My brother
- Ready Player One on Scribd >>> While listening to a Geek’s Guide to the Galaxy podcast
- Sam Harris’s podcast on his website >>> Enjoyed reading Sam’s book, then checked out his podcast
- A Way with Words podcast on Overcast.fm >>> My friend
- This video on YouTube from Simon Sinek >>> Up Next feature/algorithm
- This podcast featuring Sierra DeMulder on Soundcloud >>> Discover tab of Soundcloud
- 99% Invisible on Tung.fm >>> In the founder’s activity feed
In summary, a combination of family/friend recommendations and algorithms, with a major lean toward family/friend recommendations when we factor the length of my committed attention to audiobooks and a small number of podcasts.
Notice that I didn’t just decide to listen to any of these from “first principles”. Even discovery is a recommendation of a sort.
I suggest doing this short exercise yourself to help you understand where you are getting your recommendations from.
It’s easy to believe we are making decisions — but are you really making a decision when your options are already vastly filtered and reduced?
Next, I wanted to take a look at Overcast.fm and Tung.fm, two podcast player apps, to understand if their social features might help me discover podcasts I wouldn’t have otherwise found through algorithmic recommendations.
Overcast.fm
Marco Arment started working on Overcast in 2013 with the overarching goal of supporting creators and improving the podcast-listening experience.
He entered a crowded app marketplace in 2014 and has steadily grown his userbase. When deciding what app to use, Marco’s story is actually pretty convincing.
But since we are focusing on Discovery, I want to look at the features.
One useful note from the YouTube video above: Marco’s intentions and design will likely continue to supporting the creators and listeners over advertisers. This is a good thing for relevant podcast discovery on his platform.
Recommendations
Here we see that I’m given recommendations from Twitter, Most Recommended, then Category-specific recommendations.
I do quite like the Twitter recommendations because I should get somewhat relevant recommendations. This is a good use of Core Drive 5: Social Influence & Relatedness. Then again, I may have to improve my twitter followers :).
These are simple Top Charts and Leaderboards.
From a Discovery standpoint, by choosing these I know others have recommended them or that they are somehow popular. I have to dig deeper into the descriptions to get a sense of whether they actually would be interesting to me.
Choosing New Podcasts
In the second image, we can see I’ve clicked through into a specific podcast. Here I can find info on individual episodes by clicking on the info icon.
It’s taking me quite a bit of time to figure out if this is something for me or not.
I kind of just want to sample an episode, you know?
Otherwise, the UI is clean. But remember, we’re talking about discovering new podcasts in this article.
Tung.fm
(Note: Tung.fm has since shut down. This analysis is preserved as a case study in social podcast design.)
Tung’s homepage describes what it is trying to be: a social podcast player.
I spoke to Jamie, founder of Tung.fm, over the phone to understand what his vision was in building Tung.
The Vision
It was simple. He thought: Why isn’t there an app that you can see and listen to what your friends are listening to?
And so he set out to build Tung in 2015.
He wanted an app that gives the user the ability to see and listen to what her friends are listening to.
In my experience with the app, I was pleasantly surprised not to be asked what my favorite categories were.
Instead, it was up to me to discover them for myself. (Of course, I do have some subscribed-to podcasts I added right away.)
Jamie backed up this first impression. He talked about how — counter-intuitively — algorithms can actually reduce discovery.
Let’s imagine that during Onboarding I choose several categories, like Science and Technology or Storytelling. In this pre-selection I’m already limiting the types of podcasts I’m exposing myself to later.
Focusing on Social
Jamie described a few other features. I’ll focus on the social aspect which leads to discovery (Core Drive 5: Social Influence & Relatedness).
- Share clip: gives you the chance to taste the podcast instead of investing a 20-minute listen [CD7]
- Make comments: interaction with other users on a specific episode of the podcast with timestamps [CD5]
- Follow user: the chance to view other listeners’ activity feeds [CD5]
- Jump between episodes: better navigation [CD3, CD4]
- Save for offline playback: improving usability [CD4]
- Position remembered: improving usability [CD4]
- Reminders for new episodes: pings user when episodes release of subscribed podcasts [CD2/4]
- @ mentions: the ability to interact with other users more easily through comments or recommendations [CD5, CD7 for recipient]
Some features he still wanted to add:
- Share profile link: to improve social connection [CD5]
- Create playlists: and sharing them [CD3, CD5 when you share them]
I also asked Jamie what he was going to build next, and how he decided what to build next.
Since he built Tung.fm solo, he had to think carefully about how to use his time. He said the next step was building an Android version.
As for me, I enjoyed using the comment features and interacting a bit with other listeners (CD5).
I used the activity feeds to browse what my friends and influencers were actually listening to in real time (CD5 and CD7).
I also liked the ability to clip and share (this wasn’t possible on Overcast). I could embed clips, tweet them with twitter card support and they would play instantly.
I could also play a podcast from a timestamp. For example, I could long-press on a clip or comment in the feed, and then have the option to play it from that timestamp.
Is Social the Future of Podcast Discovery?
The real question is how much do users really want social embedded in their podcast experience?
It’s true we’re getting recommendations from many places. Why not simply add this in to the experience, as Tung was doing?
Tung.fm eventually shut down, but the idea didn’t die. Spotify added collaborative playlists and social listening features. Apple Podcasts built shared-with-you integration through iMessage. Even YouTube — now a major podcast platform — leans on subscriber feeds and community posts as social discovery layers.
The Octalysis lesson here is straightforward: algorithms are Core Drive 2 machines — they optimize for what you’ve already accomplished (listened to). Social discovery activates Core Drive 5 (you trust your friend’s taste) and Core Drive 7 (you never know what they’ll recommend next). That combination produces serendipity — exactly what discovery should feel like.
The platforms that figure out how to blend both — algorithmic precision with social serendipity — will own the next era of audio.
Related Reading
- The Octalysis Framework for Gamification & Behavioral Design
- Core Drive 5: Social Influence & Relatedness
- Core Drive 7: Unpredictability & Curiosity
- Gamification Analysis of Snapchat: Camera and Storytelling
- What is Gamification?
- Trello vs. Pivotal Tracker: A Gamification Analysis
- Books by Yu-kai Chou