In the past decade, podcasting has transformed from an obscure medium for hyper-tech insiders into a media format that even your grandma can tune in to. They’ve come a long way, but podcasts have even further to go. What’s in store for the next decade of podcasting and radio? Read our 10 predictions for the next 10 years of podcasting.
Back in January we asked: What are the biggest challenges facing digital audio? Nick van Der Kolk, host of Radiotopia’s Love + Radio said: “The biggest vacuum right now is curatorial.” He wasn’t the only one: lots of people have noted the barriers that keep great audio from being discovered.
The conversation around audio storytelling can be diffuse. So how do you keep track of the threads? Through our project Audiosear.ch, we’ve been experimenting with new ways to discover audio.
FIND AUDIO ON TRENDING TOPICS AND PEOPLE
Our “trending” feature takes podcast topics and matches them with what people are talking about on social media. The picks aren’t limited to new audio: by searching across Audiosear.ch content, we can find the whole audio history of a topic. For example, by identifying all the audio related to the release of the new Apple Music app, Audiosear.ch shows you the evolution of the conversation, from early discussions of its technology, to Taylor Swift’s game-changing tweets about Apple.
FIND AUDIO STORIES RECOMMENDED BY PEOPLE
In an informal survey earlier this year, we asked how people discovered podcasts. We were unsurprised to see that good old “word of mouth” is still the most frequent way listeners get turned on to new audio shows. Our “tastemaker” feature captures that human judgement about what’s good by pairing audio stories with quotes from the people who recommend them.
So where are people recommending audio online? We’ve found that podcast power-listeners take to Twitter, Reddit, and email newsletters to talk about what they’ve been listening to.
By using trending topics to guide listeners to topical audio, and aggregating social recommendations into one place, we’re chipping away at the challenges of audio discovery. It’s a work in progress: check out trending and tastemaker audio on Audiosear.ch — and let us know what we’re missing!
*not a real word
by Shindo N. Strzelczyk, software engineer at Pop Up Archive
Podcast enthusiasts have a hard time talking about the thing they love. There is even disagreement about whether to use the Apple-centric term “podcast,” or the too-broad term “radio,” or the vague and generic-sounding “digital radio,” to describe, precisely, digital spoken audio on the internet. Another problem is that this medium is still relatively new and only recently gaining popularity, so the discourse surrounding it is unsettled. In spite of its newness, there are over 250,000 podcasts in the iTunes store, comprising more than 8,000,000 episodes, and almost all of that content is opaque and unsearchable. So, if you want to know what’s happening right now in the “podcastphere” (for lack of a better word), you are pretty much out of luck.
Our newest project, Audiosear.ch, is an attempt to index, analyze, and interpret all of the data about podcasts on the internet, and make that data publicly available through our API. Part of that process involves generating a high quality automatic transcripts and extracting entities related to the content. We have now identified over 10,000 people from those entities and begun to determine whether they are a host, producer, guest, or topic of conversation in shows and individual episodes.
Our methods are still exploratory and experimental, but now that we have amassed a wide selection of podcasts from various sources, totaling more than 190,000 minutes of about 6,400 episodes, we can begin to analyze trends and patterns in the data.
Our first data visualization is a pair of charts of the most-mentioned people in our database over the last five months, grouped by week. The full interactive visualization allows you to change the time range and isolate the data for individual people.
The nature of this data presents several challenges when attempting to quantify it. For example:
- Varying timelines of publication. We decided to group podcasts by week since that gives a relatively good estimate of total mentions in a given time period. But podcasts are produced at various intervals ranging from daily to monthly, so it isn’t necessarily the case that the person is being mentioned at a particular time for similar reasons.
- People with multiple aliases. You may also notice the conspicuous absence of Hillary Clinton in this list which includes many politicians. That is because our people entities are named in reference to Wikipedia, and that site uses her full name: Hillary Rodham Clinton. So, when our software scrapes our transcripts, it’s looking for “Hillary Rodham Clinton,” even though that’s not how people usually refer to her. (We are currently working on identifying aliases of people.)
As we continue to grow our catalog of podcasts and improve the accuracy of our data, we hope others will follow our lead and utilize it to begin to crack open the secrets of the podcastphere. And if you can come up with a better word for it, please tweet it to us.