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The Platforms of Podcasting: Past and Present

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This article explores the role of digital platforms in podcasting (both past and present) and their impacts on the emergent podcast industry structure, content, and governance. Nieborg and Poell’s theoretical framework for understanding the impacts of platformization on culture is leveraged here to better understand the changes underway in podcasting. Like other forms of media, podcasting is being profoundly reshaped by platformization, though these transformations are distinct from other media in several key ways. Because podcasting emerged as a technology at the beginning of the 21st century before the advent of social media and the cloud, its decentralized architecture is structured around RSS, also known as “Really Simple Syndication.” When Apple added RSS aggregation into their iTunes Music Store in 2005, their market dominance in digital audio sales shaped early popular conceptions for the medium. I then outline how platformization is reshaping podcasting today by exploring how the three primary functions of media-related platform services—storage, discovery, and consumption—are shaping producers’ and audience experiences. Market imperatives for audience consumption data, as well as the structural features of platforms, are currently fueling industry consolidation. Even though podcasting is built upon the open architecture of RSS, commercial pressures and the desire of market players to capitalize on the “winner-take-all” features of platforms are shaping the trajectory of the medium’s current development.
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Platformization of Cultural Production
Podcasting is expanding rapidly as a popular cultural phe-
nomenon, connecting listeners to audio content created by
professionals, radio stations, and amateur hobbyists. Recent
data from Edison Research revealed that an estimated 73 mil-
lion Americans had listened to a podcast in the previous
month, and those audiences listened to an average of seven
podcasts per week (Edison Research, 2018). Encouraged by
the huge success of the 2014 podcast sensation Serial—which
was downloaded more than 80 million times in the first 6
months (Mallenbaum, 2015)—entrepreneurs and legacy
media companies with commercial interests in broadcasting
have rapidly expanded their commercial interests in podcast-
ing, bringing professional standards and the logics of capital
with them. The increased visibility of podcasting in the past
10 years is due in no small measure to the market power
Apple’s digital platform (specifically its Apple Podcasts
directory, iOS mobile operating system, and hardware such as
iPods and iPhones). For example, podcast hosting firm
Blubrry (2017) reported that approximately 56% of all pod-
casts it hosted in 2017 were downloaded or accessed via
Apple’s platform. After years of requests, in late 2017 Apple
began releasing some limited forms of podcast consumption
data to its users (Kafka, 2017; Webster, 2017). Other major
tech companies such as Google and Spotify have also recently
integrated podcasting into their own existing music services,
further expanding the potential reach of the medium. Podcast
hosting firms such as Stitcher, Libsyn, Blubrry, and Podbean
(among others) have also become important players in the
podcasting ecosystem by lowering offering ancillary produc-
tion and data analytical services to podcasters.
This essay explores the role of digital platforms in pod-
casting (both past and present) and their impacts on the emer-
gent podcast industry structure as well as its content and form.
As suggested by Srnicek (2016), the key leverage provided by
platforms stem from the ability of these services to “monopo-
lize, extract, analyze, and use the increasingly large amounts
of data that [are] being recorded” (p. 43). In the case of pod-
casting, the market imperative for audience consumption
880002SMSXXX10.1177/2056305119880002Social Media <span class="symbol" cstyle="Mathematical">+</span> SocietySullivan
research-article20192019
Muhlenberg College, USA
Corresponding Author:
John L. Sullivan, Muhlenberg College, 2400 Chew Street, Allentown, PA
18104, USA.
Email: johnsullivan@muhlenberg.edu
The Platforms of Podcasting:
Past and Present
John L. Sullivan
Abstract
This article explores the role of digital platforms in podcasting (both past and present) and their impacts on the emergent
podcast industry structure, content, and governance. Nieborg and Poell’s theoretical framework for understanding the
impacts of platformization on culture is leveraged here to better understand the changes underway in podcasting. Like other
forms of media, podcasting is being profoundly reshaped by platformization, though these transformations are distinct from
other media in several key ways. Because podcasting emerged as a technology at the beginning of the 21st century before
the advent of social media and the cloud, its decentralized architecture is structured around RSS, also known as “Really
Simple Syndication.” When Apple added RSS aggregation into their iTunes Music Store in 2005, their market dominance
in digital audio sales shaped early popular conceptions for the medium. I then outline how platformization is reshaping
podcasting today by exploring how the three primary functions of media-related platform services—storage, discovery, and
consumption—are shaping producers’ and audience experiences. Market imperatives for audience consumption data, as well
as the structural features of platforms, are currently fueling industry consolidation. Even though podcasting is built upon
the open architecture of RSS, commercial pressures and the desire of market players to capitalize on the “winner-take-all”
features of platforms are shaping the trajectory of the medium’s current development.
Keywords
podcasting, platform, Apple, RSS, distribution, audio, media industries
2 Social Media + Society
data, in particular, is fueling industry consolidation among
these competing platforms, though Apple’s early dominance
makes it the most likely beneficiary of podcast platformiza-
tion. The increased centrality of platforms like Apple
Podcasts, Spotify, and Google Podcasts will make it more
likely that discovery takes place via these platforms. These
major distribution platforms are increasingly operating as
gatekeepers to audiences by canalizing audience attention
into a smaller number of high-profile shows. As I note in the
conclusion, podcasting’s early roots as a service built upon
the open architecture of RSS (Really Simple Syndication)
would seem to blunt the forces of platformization gripping
other forms of online media. However, commercial pressures
and the desire of market players to capitalize on the “winner-
take-all” features of platforms may begin to take a more
active role in shaping the trajectory of the medium’s current
development.
Podcasting and Platformization
Platforms can be understood at their most basic level as “digi-
tal infrastructures that enable two or more groups to interact”
(Srnicek, 2016, p. 43). These infrastructures act as intermedi-
aries between different types of users, including customers,
suppliers, producers, service providers, suppliers, and adver-
tisers. Gillespie (2018) offers a similar definition, noting that
platforms can be understood as “sites and services that host
public expression, store it on and serve it up from the cloud,
organize access to it through search and recommendation, or
install it onto mobile devices” (p. 254). Platforms consist both
of the technical infrastructure that allow for sharing of infor-
mation as well as a set of rules (governance) that enable and
constrain particular types of user activity. The shared technical
infrastructure and open nature of these platforms1 often pro-
vides them with the aura of neutrality, much like a utility or
common carrier service. Platforms are much more than neutral
arbiters of interactions and transactions, however. By shaping
the types of interactions among their participants in differen-
tial ways, platforms can also shape “how modularity and
power are negotiated between a core unit with low variability
and heterogeneous components of high variability” (Plantin,
Lagoze, Edwards, & Sandvig, 2018, p. 298).
In a Web 2.0 era, platforms have become a focal point for
scholarly inquiry due to their increased centrality in the cre-
ation and distribution of media. The financial success of
major sites like YouTube, Facebook, and iTunes has encour-
aged the proliferation of new platforms in the marketplace.
Nieborg and Poell (2018) have identified these changes as
platformization, or “the penetration of economic, govern-
mental, and infrastructural extensions of digital platforms
into the web and app ecosystems, fundamentally affecting
the operations of the cultural industries” (p. 2). Their model
emphasizes three specific aspects of platformization: how
platforms shift larger market structures, how cultural produc-
tion is governed through platforms, and how platformization
transforms the infrastructure of cultural production (via data-
oriented practices such as algorithms, data structures, soft-
ware development kits [SDKs], and application programming
interfaces [APIs], among others). This last element, they
note, refocuses attention on the “actual production and cir-
culation practices” that are both enabled and constrained by
platforms (Nieborg and Poell, 2018, p. 8). Nieborg and
Poell’s theoretical framework is particularly useful for under-
standing the impacts of platformization on culture and is lev-
eraged here to better understand the changes currently
underway in podcasting. Like other forms of media, podcast-
ing is also being reshaped by platformization, though these
transformations are distinct from other types of media largely
due to the openness of its technical infrastructure.
An Open Architecture: RSS as
Anti-Platform
Podcasting can be understood as
a technology used to distribute, receive, and listen, on-demand,
to sound content produced by traditional editors such as radio,
publishing houses, journalists, and educational institutions . . . as
well as content created by independent radio producers, artists,
and radio amateurs. (Bonini, 2015, p. 21)
One of the distinguishing features of podcasting is the open-
ness of its distribution mechanism. Because it emerged as a
technology at the beginning of the 21st century before the
advent of social media and the cloud, podcasting features a
decentralized technical architecture, whereby audio content is
stored all over the web and linked together via RSS, also known
as “Rich Site Summary” (or “Real Simple Syndication”). RSS
was initially developed by Dan Libby and Ramanathan V.
Guha at Netscape in 1999 as a text-based tool for allowing
users to get automatic updates from blogs and other websites.
As an open technical standard, RSS is free and allows listeners
(via a helper app or “podcatcher”) to locate, subscribe, and lis-
ten to new content without the necessity of visiting a specific
storage platform or website (Markman & Sawyer, 2014, p. 20).
Given its later significance in the popularization of pod-
casting, it is worth noting that the seeds of RSS were origi-
nally sown within Apple itself. Guha had developed a
precursor software project in 1995 called the Meta Content
Framework (MCF) while he was a software developer at
Apple. MCF was intended to function as a means to describe
and catalog metadata about web pages, but when Steve Jobs
returned to Apple in 1997 as its CEO, he shut down much of
Apple’s research activity. Guha then began developing a new
version of MCF at Nescape called the Resource Description
Framework (RDF), which was based in the new XML format
(Hammersley, 2003, p. 2). Netscape popularized the first use
of RDF Site Summary (RSS) in its browser by displaying
headlines and links from other web pages within a single
window, something that was unique to its browser.
Sullivan 3
RSS was eventually abandoned at Netscape and left to the
developer community. Dave Winer, CEO of Userland
Software, released RSS version 0.91, though this competed
with a more complicated and enhanced version of the software
(RSS 1.0) championed by Rael Dornfest at O’Reilly
(Hammersley, 2003, p. 4). The innovation of RSS feeds for
podcasting occurred in 2000, when Dave Winer discussed
with former MTV VJ Adam Curry the possibility of leverag-
ing web content syndication for video, though the technology
would be later implemented with audio files (Cochrane,
2005b, p. 8). Winer developed the concept of “enclosure,”
whereby URL addresses could be passed along to an aggrega-
tor. In September 2003, Winer created a feed with enclosures
and married it to a software script he wrote called “iPodder”
that would move MP3 files from Userland Radio’s website to
Apple’s iTunes software (Wikipedia, 2017) and finally to his
own iPod for listening. Curry’s iPodder was the first pod-
catcher to leverage RSS feeds to allow for syndicated audio
web content, or podcasting. The addition of media enclosures
to RSS by software developer Dave Winer and Adam Curry
allowed for media aggregators to locate and create directories
for web content via the feed (For a full discussion of the early
history of Internet audio, see Berry, 2006; Bottomley, 2016;
Sterne, Morris, Baker, & Freire, 2008). The word “podcast-
ing” was not popularized until 2004, when journalist Ben
Hammersley (2004) coined the term in an article in The
Guardian (he also suggested the term “audioblogging”).
Unlike many platform-based cultural forms available on
the web today (such as YouTube for video, Flickr for photos,
and Facebook for social data, for example), wherein the plat-
form acts as a centralized repository of data through which it
connects producers and audiences, podcasting data files are
scattered across the Internet and connected together via RSS
links. Key to the early popularization of the podcasting was
the ability of programmers like Winer and Curry to leverage
the open RSS standard to write new code (iPodder) that would
enhance its features by allowing it to interface with existing
audio playback software (iTunes) and hardware (iPods).
Apple’s iTunes and Early Podcast
Platformization
Podcasting has been inextricably linked to tech giant Apple,
which was chiefly responsible for popularizing it in the
early 2000s. In the first few years of its existence, users
were required to cut and paste RSS links into podcatching
software to download audio files and syndicate (or “sub-
scribe to”) a podcast. In an era before social media giants
like Facebook and Twitter, RSS feeds were popularized via
individual websites, through blogs, traditional news web-
sites, and via several new online directories that were
launched in 2005. Two of the most popular directories of
the time were PodcastAlley.com and PodcastPickle.com
(Cochrane, 2005a). Thematic podcast networks also
began to emerge, such as techpodcasts.com, which featured
technology-themed podcasts. Users during this era were
required to utilize a multistep process to successfully listen
to a podcast: first, locate the podcast via one of these small
directories; second, copy the RSS feed address; third, paste
it into podcatcher software; and finally, download the audio
file to the computer for playback.
While RSS made it technically possible for users to sub-
scribe via podcatcher software like iPodder, the process was
cumbersome and not well understood outside communities
of tech enthusiasts. When Apple CEO Steve Jobs announced
the release of iTunes 4.9 at the Apple Worldwide Developers
Conference on 28 June 2005, he trumpeted its ability to pro-
vide easy access to audio podcasts, calling podcasting “TiVO
for radio.” Unlike iTunes’ music store, which sold music
MP3 files direct to consumers, iTunes operated as a visually
attractive and easily navigable podcatcher, allowing users to
subscribe to RSS-enabled audio feeds via Apple’s software
(see Figure 1). As one tech journalist recalled,
prior to the iTunes 4.9 update on June 28, 2005, podcasts were
so clumsily arranged around the internet and so technologically
challenging to use on any device other than a desktop or laptop
computer that only the most tech-savvy even knew they existed.
(Friess, 2015)
Apple’s dominant market share in digital music sales (which
reached 69% of the market by 2009) had the effect of
instantly introducing podcasts to millions of potential listen-
ers (Frommer, 2009). In essence, “iTunes 4.9 effectively
brought podcasting into the cultural mainstream” (Bottomley,
2015, p. 164).
The structure of Apple’s initial podcatcher interface and
governance structure shaped the development of podcasting
distribution in several important ways. On the surface, the
iTunes version 4.9 podcatcher software interface was virtu-
ally identical to that of iTunes’ music store, with a search bar
interface to facilitate keyword searches, a list of “Top
Podcasts” indicating popular or most downloaded shows, and
thematic categories of podcast content. To harmonize the
podcasts section of iTunes with its music store counterpart
(which featured music album covers), Apple introduced cover
art for their podcasts. This encouraged producers to create
visually stimulating identifiers for podcasts (all others were
given a generic RSS icon), which, in turn, shaped consumers’
expectations for podcasts. In his keynote address announcing
iTunes Version 4.9, Steve Jobs’ enthusiasm for podcasting
was specifically linked to larger companies’ content (includ-
ing major radio broadcasters, network broadcasters, maga-
zines, newspapers, and companies like Disney, Proctor &
Gamble, Ford, and General Motors) rather than amateurs.
Content from these institutional content providers was given
prominent space on iTunes, which allowed consumers to dis-
cover it more readily. This linkage created by Steve Jobs and
Apple—between podcasting and professionalized, corporate
4 Social Media + Society
media—bears more than a passing resemblance to David
Sarnoff’s RCA in the early days of radio. In the late 1920s,
RCA effectively defined the practice of radio broadcasting as
a corporate one-to-many experience, rather than an audio
exchange among everyday citizens or creative amateurs (see
Sterne et al., 2008). Finally, unlike its music store counter-
part, Apple’s pass-through of RSS feeds meant that all pod-
casts were distributed for free on iTunes. Since Apple did not
choose to host the audio data files for download (thereby
essentially adopting the decentralization model of RSS), it
also thereby rendered paywalls, pay-per-download, or other
monetary exchanges for podcasting impossible via iTunes.
This was one of the factors that pushed early podcasters to
pursue advertiser-supported revenue models. Apple’s iTunes
was the first service to offer what scholars note as a “plat-
form” service for podcasting, in that it provided a centralized
repository of podcasts for ease of discovery, while also
enabling podcasters to easily reach audiences by bundling
their respective RSS feeds into a seamless digital interface.
Podcasting’s Post-2005 Platform
Proliferation
The first element of Nieborg and Poell’s (2018) model con-
cerns the impacts of platform infrastructure on market struc-
tures. In the case of podcasting, the decentralized infrastructure
of RSS is largely responsible for its fragmented structure
today. Since 2005, there has been a proliferation of podcast
platforms. While this level of fragmentation would seem to
defy the centripetal pull of platformization, recent moves
toward greater centralization are slowly altering the status
quo. There are three primary functions of media-related plat-
form services: storage, discovery, and consumption. Typical
Web 2.0 platforms, such as YouTube, Flickr, Netflix, or
Amazon Prime, encapsulate all three of these functions: they
present content for users to discover or search through their
interface; they serve as a data repository for the files to be
delivered to the user (whether via download or streaming);
and they offer embedded playing software to allow users to
consume media. Other types of interactions are made possible
by platforms, of course, such as sociality, branding, and
advertising, but I argue that storage, discovery, and consump-
tion are the core functions without which content platform
services are incomplete. In the case of podcasting, these three
functions have been often (though not always) separated into
different services. As the primary distribution mechanism,
RSS is simply a text file that points to content housed else-
where on the web. Consequently, directory services like
Apple Podcasts (Apple’s new name for its iTunes podcatcher)
point to the content rather than storing the audio or video
files. Podcast data—the MP3 audio file—is typically stored
on a traditional web host or on a dedicated podcast host site.
Second, the content discovery function in podcasting is
typically served by podcast directories that organize podcasts
into categories along with keyword search capability. Third,
listeners must rely upon software to consume digital audio
Figure 1. Apple’s initial podcatcher software in June 2005 (iTunes, version 4.9).
Source. Image Used With Permission of AskDaveTaylor.com.
Sullivan 5
content. On one hand, this separation of functions into sepa-
rate services has lowered barriers for market entrants, creat-
ing a rush of new competitive services specializing in one or
more of these functions. On the other hand, the rush of com-
panies and entrepreneurs to enter the podcasting market has
created a somewhat confusing and chaotic landscape for both
consumers and podcasters. The drive to streamline the pro-
cess of podcast discovery and consumption to maximize audi-
ence size (to capture advertising revenue) is one of the major
driving forces behind the increasing platformization of the
medium today. While these core functions are outlined sepa-
rately below for the purposes of analysis, it is important to
note that they are often less clearly demarcated in practice,
with some podcast services encompassing more than one
function. These three functions and the current podcast plat-
formization trends found in each are briefly considered below.
Storage Platforms and Consumption Metrics
Podcasting requires that audio data files are stored on servers
and hosting services. There are a plethora companies hosting
data files, many of which cater to the specific needs of pod-
casters. Libsyn (short for “Liberated Syndication”), launched
in 2004, was the first dedicated podcast web hosting com-
pany, followed in 2005 by Blubrry. These services typically
offer podcasters an array of features in addition to the storage
of audio files and the management of the podcast RSS feed.
Blubrry, for example, developed a plug-in entitled PowerPress
in 2008 to allow its customers to more easily create and
maintain an RSS feed from their WordPress web pages and
blogs. Other podcast hosting services have developed similar
WordPress plug-ins. As interest in podcasting has risen dra-
matically, there has been a rapid expansion in podcast host-
ing companies, all of them promising to streamline the
process of storing podcast audio files, maintaining and vali-
dating podcast RSS feeds, and registering podcasts on the
major directories (discussed below). These hosting compa-
nies include Podbean (launched 2006), Podomatic (launched
2008), BlogTalkRadio (launched 2008), Audioboom (laun-
ched 2009), Buzzsprout (launched 2009), Spreaker (launched
2009), Simplecast (launched 2013), Fireside.fm (laun-
ched 2015), and Castos (launched 2017), among many others.
Like all forms of web hosting, there is a monthly subscrip-
tion cost associated with podcast hosting (often graduated
according to the number of downloads), so the distributed
infrastructure of RSS ironically may create an initial finan-
cial barrier to new podcasters.2 Podcast hosting companies
are typically the first point of contact with independent pod-
casters, and they are the strongest podcast evangelists, since
their business depends upon increasing the number of new
content producers. With the rising commercial potential of
the medium, some consolidation has begun to among pod-
cast hosts. In 2017, for instance, BlogTalkRadio merged
with Spreaker, and recently both companies were bought out
by Voxnest.
A more recent entrant to podcasting, Anchor.fm, launched
to fanfare at South by Southwest festival in 2016, has staked
its business model on providing its users a free, “one stop”
solution to podcasting (Shontell, 2016). Anchor.fm enables
users to record audio directly via a smartphone app, edit the
file and add music via app, upload it to Anchor’s servers, and
distribute it directly to major directories like Apple Podcasts,
Google Play Music, and Spotify, all for free. Anchor.fm gen-
erates income by embedding advertising across all of its
user-produced podcast content. There has been an exponen-
tial growth in the introduction of new podcasts in the last
year, and much of that can be attributed to the success of
Anchor in lowering the entry barrier to podcast production.
As Figure 2 illustrates, despite its recent market entry,
Anchor.fm hosts 9.4% of all podcast episodes listed on the
Apple Podcasts directory, second only to Soundcloud, which
also offers free file hosting and audio streaming. For context,
it is important to note that Anchor.fm’s share of podcasts
eclipses Libsyn, which has a long history as one of the first
companies to host podcast RSS feeds.
Hosting companies are key players in the podcast market
because their platforms allow them to gather valuable data on
content consumption. Specifically, when content directories
(like Apple Podcasts) reference the RSS feed to signal the
transfer of an audio file, the podcast host registers the down-
load from its server and notes the distribution source for the
download as well as the operating system (desktop, iOS, or
Android). Although podcast metrics are still in their infancy
and there has been much debate among industry players about
the appropriate consumption data, one key measure has been
“downloads per episode” (DPE) as reported by the podcast
Figure 2. Podcast media hosting services (all time).
Source. Chartable.com (2018).
6 Social Media + Society
host. In 2017, hosting companies, along with advertisers, ad
agencies, podcast networks, and representatives from public
radio, collaborated under the auspices of the Interactive
Advertising Bureau (IAB) to create a set of podcast measure-
ment standards (Interactive Advertising Bureau, 2017) that
were based largely on the DPE standard. This move placed
podcast hosts as the central platforms in the generation of an
industry-wide standard for podcast consumption (Fleck, 2018).
The file storage platforms, then, act as a service provider to
both podcasters and advertisers, though in distinct ways.
Discovery Platforms and the Threat of Podcast
Enclosure
Since audio files are scattered around the Internet and locat-
able via their RSS feeds, the second major function of podcast
platforms is to facilitate the discovery of those podcasts and to
allow listeners to “subscribe,” which then provides them
updates when new content is added for a particular show.
Network externalities play an outsize role in podcast discov-
ery, since listeners gravitate to directories that allow for great-
est ease of discovery and that have the most comprehensive
directories (network externalities are also key for search
engines, and account for the huge market advantage of Google,
for example). Discovery platforms typically do not store the
audio files on their servers, and instead pass through the MP3
file that is accessed from the podcast host server. Like the MP3
file itself, the RSS feed acts as a “container technology”
(Sterne, 2006). The most dominant directory is Apple Podcasts
(formerly iTunes), which has enjoyed a significant “first to
market” advantage such that most podcast listeners in the
United States today are accessing those shows via Apple’s
directory (Interactive Advertising Bureau, 2017). While there
are over a hundred podcast directories online, there are only
two other significant discovery platforms: Google Play Music
and Spotify. Professional and indie podcasters are often bewil-
dered at the panoply of distribution outlets. Most aim to
include their shows in as many discovery platforms as possible
to maximize audience traffic. This has fueled the growth in
podcast hosting platforms that promise a “one stop” solution
for distribution via the largest directories.
Nieborg and Poell’s (2018) model draws attention to the
realignment of technical infrastructures thanks to the
expanded reach of platforms via algorithms, SDKs, and
APIs. In the case of podcasting, Apple’s dominance as a dis-
covery platform is even greater thanks to its API, which
extends its directory service beyond its own Apple Podcasts
service. Popular mobile podcast consumption apps such as
Overcast, Pocket Casts, Downcast, and Podcast Addict all
utilize Apple’s directory for listing podcasts by linking their
apps to the Apple Podcasts API. Apple’s terms of service also
introduce a form of editorial control over the content and
presentation of podcasts on its directory. Recently, podcast
hosts have received complaints about their shows being “de-
listed” from Apple Podcasts for violating its rules or terms of
service. For example, Apple does not allow its name or the
names of its products to be in any podcast title, it blocks long
podcast titles, and it prevents URL links from podcast show
notes and even polices cover art images (Cochrane &
Greenlee, 2018). Apple Podcasts currently acts also as an
archive for podcast shows, allowing listeners to discover
shows that are no longer being actively produced (something
that podcasters call “podfaded” shows). Some estimates are
that perhaps only half of the more than 500,000 podcasts
listed on Apple Podcasts have posted new content in the past
3 months (Goldstein, 2018). If Apple were to automatically
“de-list” podcasts that had not recently updated their content,
for example, Apple Podcasts would look quite different and
much content would become almost impossible for listeners
to find. Podcasting’s dependence on platform APIs like
Apple’s is similar to trends in other social media such as
Facebook and Twitter, wherein upstream decisions by the
platform about information distribution can have profound
consequences for downstream services, apps (Kastrenakes,
2018), and even media scholars (Burgess & Bruns, 2012).
Along with the power of discovery platforms to direct lis-
teners’ attention, they also have the potential to create artificial
scarcities of content to maximize the potential for revenue. For
example, the business press has been excitedly suggesting for
some time that the “Netflix of podcasting” moment has
arrived, whereby platforms serve up “premium” or “exclu-
sive” audio content to listeners that requires a subscription fee
(Nagy, 2015; Porch, 2018; Rowe, 2017), although some are
more circumspect about this potential (Quah, 2018; Rosenblatt,
2018). Podcasters have largely approached the notion of
“Netflix-ization” with scorn and derision (Fang, 2018). In
their eyes, podcasting will never transform into a subscription-
based service due to the open architecture of RSS: How can
you take something that is freely distributed via RSS and
essentially lock it up behind a paywall?
The reality is, however, that platform enclosure—the cre-
ation of “walled gardens” of content available only to monthly
subscribers—is slowly taking hold in podcasting. Ironically,
indie podcasters themselves have paved the way for enclosure
by adopting similar techniques for monetizing their own pod-
casts: that is, by offering “premium” content that is available
only via a monthly subscription. Larger industry players are
replicating these strategies in an attempt to either lure more
customers to their platform, or to “lock in” existing customers
by encouraging them to access podcast content via their exist-
ing service. How can platform enclosure take hold in a
medium that is built upon the open standard of RSS? The
answer is that major audio content platforms are slowly steer-
ing listeners away from RSS-delivered audio content and into
premium content that is platform exclusive. Existing podcast
networks such as Howl Premium and giant Stitcher Premium,
for example, offer listeners paywalled content that is other-
wise not available to listeners on other discovery platforms.
Luminary—a new venture–capital-backed podcasting service
that launched in early 2019—offers only exclusive, paywalled
Sullivan 7
podcast content by media celebrities such as Trevor Noah and
NPR’s Guy Raz and will not make the content available via
RSS (Rottgers, 2019). Music streaming giant Spotify has
made perhaps the most aggressive moves in favor of platform
enclosure. For example, it introduced three new podcasts in
2017, and these shows were only available on its streaming
app, and not via RSS (Roettgers, 2017). Spotify went a step
further in 2018 and secured a deal to exclusively distribute the
Joe Budden Podcast, a popular hip-hop music podcast
(Saponara, 2018). Spotify’s deal with Joe Budden was note-
worthy because it stipulated that new episodes of Budden’s
podcast would not be distributed via RSS but would instead
be found only on Spotify. Spotify’s deal is the first that would
essentially remove content that was previously distributed via
RSS and lock its distribution into a proprietary platform. This
move is not dissimilar to the recent strategic alliance between
Facebook and the New York Times, where readers increas-
ingly link to online news via the social media giant at the
expense of the open framework of RSS (Plantin et al., 2018).
Finally, in February 2019, Spotify stunned the podcasting
industry by announcing its $337 million acquisition of Gimlet
Media—the podcast production company cofounded in 2014
by NPR veteran Alex Blumberg—and Anchor.fm, the fastest-
growing podcasting platform (Spangler, 2019; Szalai, 2019).
Spotify’s aim is likely to capitalize on the high production
values and audiences for Gimlet’s podcasts by bringing its
listeners to Spotify’s platform and away from other discovery
platforms like Apple Podcasts and Google Podcasts. As pod-
casting becomes less dependent on the open RSS standard for
distribution (see Figure 3), podcast directories and streaming
platforms are aiming to shift distribution away from open
infrastructures and toward their own services to maximize the
“winner take all” functions of platforms. In other words, once
RSS is no longer required to distribute podcasts, the threat of
platform enclosure will only increase.
Consumption Apps and Platform Alliances
The third important function of online media platforms is to
allow users to consume the content. In the early years of pod-
casting prior to the introduction of the iPhone in 2007, most
listeners consumed podcasts either on their computers or on
dedicated digital audio devices such as iPods. In an increas-
ingly mobile era, however, podcast consumption has shifted
to mobile devices, mediated largely via the iOS and Android
mobile operating systems. The Interactive Advertising
Bureau (2017) found that roughly 45%–52% of all podcast
consumption in the United States was achieved via the Apple
Podcast app on iOS. The inclusion of a default, preinstalled
podcast app in Apple’s iOS Version 8.0 update in 2014 was a
significant factor in expanding the audience for podcasting,
resulting in 13.7 billion episode downloads on that platform
in 2017 alone (Locker, 2018). This dramatic increase in pod-
cast listenership over such a short period of time demon-
strates the power of apps—and the default software settings
behind those apps—to drive discovery and consumption
behaviors (for more on the power of software defaults, see
Kitchin & Dodge, 2011; Shah & Kesan, 2008; Shah &
Sandvig, 2008). While Apple’s iOS podcast app dominates,
there are numerous competing apps that allow users to sub-
scribe and consume podcasts such as Stitcher, Pocket Casts,
Overcast, Podcast Addict, BeyondPod, DoggCatcher and
Downcast, and Castbox, among many others. Adding to the
fragmentation in consumption apps, some podcast hosting
companies (like Podbean, for example) have launched a ded-
icated podcasting app. Finally, the “app”-ification of pod-
casting is also well underway, with a number of popular
podcasts such as This American Life and The Dave Ramsey
Show launching their own dedicated apps within the iOS and
Android app stores. Podcast hosting companies like Libsyn
and Spreaker have fueled appification by offering to launch
a dedicated app for their customers’ podcasts as part of their
subscription fee.
Mobile consumption apps are critical to the infrastructure
of podcasting because they provide a dual functionality as
both tools of content discovery and consumption. Many of
these apps are aligning themselves strategically and finan-
cially with discovery platforms to monetize their users’ data
and to introduce artificial scarcities. A number of mobile apps
have been increasingly become the target of acquisitions by
large media companies wishing to gain a foothold in the pod-
casting market. For example, E.W. Scripps, a traditional media
Figure 3. Google search queries for “RSS” (2005–Present).
8 Social Media + Society
company that owns a diverse portfolio of legacy media such as
newspapers, broadcast radio, and television stations purchased
podcast advertising firm Midroll in 2015 for $10 million and
quickly followed with a $4.5 million purchase of podcasting
app Stitcher in 2016 (Perlberg, 2016). Sensing the importance
of consumption apps, in May 2018 a consortium of public
radio organizations including NPR, WNYC Studios, WBEZ
Chicago, and This American Life, purchased Pocket Casts
(Mullin, 2018).
Podcast mobile apps offer their own forms of interactiv-
ity, sociability, and content curation by highlighting within
the app a unique constellation of content specifically
designed for users (Morris & Patterson, 2015). This form of
specialized curation has been increasingly influenced by
strategic alliances among consumption apps and large con-
tent providers. For instance, Castbox—a new podcast mobile
app that has already raised over $13 million in venture capi-
tal funding—has a section of its free app designated as “pre-
mium” content that is available via an in-app subscription
(Sawers, 2018). Some of the content in this premium section
is produced by Castbox itself, and the rest is produced by its
content partner, Wondery (InsideRadio, 2018). Similarly, the
iHeartRadio app (which has a very small curated list of pod-
casts in its own directory of 20,000), has leveraged its ties to
the broadcast radio industry to make aggressive moves into
podcasting. iHeartRadio purchased the HowStuffWorks pod-
cast network for $55 million on 15 September 2018, enabling
it to exclusively feature the those podcasts and paving the
way for more “premium” content for its mobile app (Jarvey,
2018). While Apple has yet to move its own podcasting
directory in the direction of platform enclosure, it is aggres-
sively pursuing this strategy in video, having invested $1 bil-
lion in acquiring and commissioning original content for a
new streaming service that will compete directly with other
video platform giants such as Netflix, Amazon Prime, and
Hulu (D’Alessandro, 2018; Mickle, 2017). These platform
alliances indicate that the fragmentation in podcasting is
slowly giving way to platform consolidation in the service of
monetization and audience maximization.
Platform Governance in a Fragmented
Ecosystem
Nieborg and Poell’s (2018) model also draws attention to the
means by which platform governance shapes the means of
cultural production. As noted above, podcasting has seen a
proliferation of storage, discovery, and consumption plat-
forms since 2005, making governance a patchwork affair.
Each of the storage and discovery platforms come with their
own terms of service (TOS) and editorial guidelines. As
Gillespie (2018) has noted, the rules established by platforms
exist primarily to protect the platform’s public brand and
profitability, though these goals are often intertwined with a
“deeply felt commitment of the platform operators for nurtur-
ing a healthy community” (p. 263). As the leading discovery
platform in the United States, Apple Podcasts’ TOS outlines
content parameters that are similar to those of other online
platforms. For example, Apple’s terms of service prohibits the
following from podcasts listed on their iTunes services:
Irrelevant content or spam.
Explicit language without setting the <explicit> tag.
Content that could be construed as racist, misogynist,
or homophobic.
Explicit or self-censored explicit language in titles,
subtitles, or descriptions.
References to illegal drugs, profanity, or violence in
the title, description, artwork, or episodes.
Content depicting graphic sex, violence, gore, illegal
drugs, or hate themes.
Third-party content or trademarks without legal
authorization or usage rights.
Source: Apple Media Services Terms and Conditions
(https://www.apple.com/legal/internet-services/
itunes/us/terms.html)
Apple’s governance structure goes beyond the above edito-
rial guidelines to include the protection of its brand identity.
For instance, Apple prohibits the words Apple Music, iTunes
Store, iTunes, Apple Podcasts, or Apple Inc. in podcast titles
or descriptions, and prohibits pictures of its corporate logos,
or of its technology (such as iPhone, iPad, or iPod) in any
cover art image in its podcast directory. Podcast hosting
companies have similar, but distinct, TOS agreements listed
on their services, but given the increase in podcast launches
post-2014 and their relatively small staffs, these services do
not closely monitor the content of the podcasts they host.
Recent podcast host entrant Anchor.fm has caused contro-
versy within the podcasting community over its TOS. Flush
with $10 million in venture capital funds (Roof, 2017), Anchor
offered itself to would-be podcasters as a free service. Veteran
podcasters and podcast hosting companies immediately began
scrutinizing Anchor’s TOS and found that the users of the site
“grant [them] a worldwide, non-exclusive, royalty-free, sub-
licensable and transferable license to use, edit, modify, aggre-
gate, reproduce, distribute, prepare derivative works of,
display, and perform the User Content in connection with the
operation of the Services.” As well-known podcaster Dave
Jackson (2018) has noted, Anchor’s claim of ownership of
user-generated content are much more restrictive than com-
parative social media platforms, including those of Twitter and
Facebook. As the fastest growing site for podcast hosting,
Anchor’s move to centralize the control of the rights to pod-
cast content points to the power of platforms to offer universal
access as a “loss leader” to monetize user-generated content,
in much the same way as Google leverages its content to sell
advertising on YouTube.
The fragmented nature of podcast platforms also makes
for a patchwork enforcement of editorial guidelines. In
2018, controversy surrounding the content of radio host and
Sullivan 9
conspiracy theorist Alex Jones’ Infowars program epito-
mized the challenges in policing content across podcasting’s
many platforms. On his Infowars radio program, which is
also released as a podcast, along with several other Infowars-
branded podcasts, right-wing host Jones was well-known
for trafficking in conspiracy theories, including one that the
Sandy Hook shooting was staged by “crisis actors.” On 5
August 2018, after complaints from Sandy Hook victims’
families and listeners, Spotify removed several episodes of
Jones’ podcast for violating its “hate speech” policy, fol-
lowed swiftly by Apple, which completely removed (de-
listed) five of its six Inforwars-branded podcasts from its
service (Vernon, 2018). Podcast host Spreaker, which stored
the audio files for The Alex Jones Show podcast, found itself
suddenly thrust into the midst of a public controversy. Citing
its own terms of service which prohibited the publication of
“any content that promotes, either directly or indirectly,
hate, racism, discrimination, pornography, or violence,”
Spreaker removed Jones’ content from its own servers fol-
lowing the moves by Spotify and Apple (Schneider, 2018).
Many podcasters took note, however, that the main Infowars
RSS feed was self-hosted on Jones’ own website, and that
listeners who wanted to access the content could simply
subscribe manually to the feed (as they had done in the early
days of podcasting) and continue to receive new episodes.
In addition, Apple faced mounting criticism for allowing the
Infowars app (which contained much if not all of the same
content) to remain in its App Store, arguing that it had not
violated its App Store TOS, which did not feature the same
language explicitly banning forms of hate speech. After a
month of heavy criticism, on 7 September 2018 Apple
relented and removed the Infowars app as well (Nicas,
2018). Apple’s choice to delist Infowars also laid bare the
symbiotic entanglements between their podcast directory
and other apps, since all consumption apps that relied on
Apple’s directory API saw Alex Jones’ content immediately
removed from their directories as well, effectively ceding
editorial control to Apple. The Infowars debacle served as
an important inflection point in podcasting because it dem-
onstrated the difficulties in managing a fractured gover-
nance structure from a multiplatform ecosystem.
Conclusion
Podcasting is in the midst of a transition. Online platforms
have begun making major investments in the medium,
hoping to attract listeners, advertisers, and new podcasters.
Thanks to the decentralized architecture of RSS, the pro-
cess of platformization outlined by Nieborg and Poell
(2018) is progressing somewhat differently than for other
large content-sharing services. Since the three functions of
content platforms—storage, discovery, and consump-
tion—are dispersed among podcast hosting companies,
directories like Apple Podcasts, and mobile apps, respec-
tively, it would appear that podcasting is less susceptible to
the forces of platformization. However, as this discussion
has outlined, the podcast market is undergoing rapid trans-
formation, spurred largely by the interest of large tech
giants like Apple, Spotify, and Google. Strategic alliances
among discovery and consumption platforms, in particular,
are moving podcasting in the direction of fully integrated
content platforms like YouTube, Facebook, and Twitter. As
Srnicek (2016) has noted, the “tendencies” that emerge
from the competitive dynamics of large platforms include
the “expansion of extraction, positioning as a gatekeeper,
and enclosure of ecosystems” (p. 98). Each of these ten-
dencies can be observed in podcasting today, at least in the
United States. While major US Internet companies Apple
and Google have already begun pushing podcasting in the
direction of platformization, recent strategic acquisitions
by Swedish audio giant Spotify point to a more platform-
centric future worldwide thanks to Spotify’s global reach.
The network effects associated with platformization
have differential benefits for listeners, content producers,
podcast platforms, and advertisers. As podcasting becomes
more popular, the number of podcasts in production has
vastly expanded, creating an increasingly crowded content
landscape. This presents a challenge for listeners hoping to
discover new shows that may appeal to their particular
interests. For listeners, then, centralized repositories like
Apple Podcasts, Spotify, or Google Play carry positive net-
work effects thanks to the ease of discovery and the crowd-
sourced curation of podcasts (much like the Rotten
Tomatoes ratings for films or Amazon’s user-generated
product reviews). Podcast networks, distributors, and
advertisers, on the other hand, derive much different ben-
efits from the network effects of platform centralization.
For these players, the major value of platformization
comes from their ability to glean more accurate consump-
tion data from a large audience for the purposes of moneti-
zation. Current audience consumption data are based
chiefly on server-side measures (downloads per episode)
and platform-side measures such as subscriptions via a
podcatchers like Apple Podcasts, Spotify, Castbox, and
Overcast, among others. What centralized platforms like
Apple Podcasts and Spotify offer to distributors and adver-
tisers is a glimpse into actual audience listening consump-
tion. How much of that podcast episode, for example, was
actually heard by audiences, and at what point did large
numbers of listeners abandon the episode? Moreover, who
is actually listening to what podcast?
Thanks to the dominance of Apple’s mobile iOS plat-
form, at least in the United States (Perez, 2018), Apple has
access to a vast trove of personal data, including valuable
information such as names, addresses, age, race, gender,
credit card numbers, and more. While Apple agreed to
release more specific consumption data to podcast produc-
ers and publishers in late 2017 (Webster, 2017), it chose to
anonymize the audience data, thus preventing would-be
podcast advertisers from employing target marketing
10 Social Media + Society
campaigns with the kind of precision that they routinely
employ on social media platforms (Willens, 2017).
Choosing to protect user-level audience data and essen-
tially give away anonymized podcast consumption statis-
tics may serve to further cement Apple’s dominance in the
podcast ecosystem. Since the value of these data will only
increase as more and more audiences access their podcasts
via Apple’s iOS platform (or via the Apple Podcasts’ API),
Apple is likely seeking to retain its long-held position as
the industry leader. Spotify’s recent acquisitions of Anchor
and Gimlet Media present a serious challenge to Apple’s
long-held platform dominance in podcasting, however. If
Spotify eschews anonymization in its metrics and links its
podcast audience consumption data to specific subscriber
accounts—including credit card data and demographics,
for example—it could offer a stronger value proposition
for podcast networks and advertisers keen on monetizing
via target marketing.
The implications of platform consolidation for indepen-
dent podcasters are potentially more negative. On the one
hand, the proliferation of storage and discovery platforms
today works to the benefit of independent podcasters and
amateurs because the ecosystem is still in considerable flux.
Competing services and new technologies are launching
almost daily, which lowers barriers to entry for new produc-
ers, particularly in the case of free services like Anchor.fm
and Soundcloud. Once this industry churn has settled and
the pace of consolidation quickens, distribution options for
podcasters may begin to dwindle, leaving them with less
autonomy to transform their hobby into full-time work on
their own terms. Once large market players like Apple,
Google, Amazon, and Spotify begin leveraging the power of
network externalities to expand the podcast audience by
offering paid subscription-based podcast content to their
large existing customer bases, professional-quality podcast-
ing (sometimes called “procasting”) will become inextrica-
bly linked to platform services. As Nieborg and Poell (2018)
note, “as cultural production is becoming increasingly plat-
form dependent, the autonomy and economic sustainability
of particular forms of cultural production is increasingly
compromised” (p. 3). Content producers wishing to reach
audiences will naturally gravitate to such services because
of their large listener base and the promotional advantages
they provide. Independent podcasters have publicly scoffed
at the idea that these platforms will ever dominate the
medium because of its reliance on an open web standard, but
content exclusivity deals like the one Spotify has struck with
The Joe Budden Podcast, its acquisitions of Gimlet Media
and Anchor, and the increasing consolidation among dis-
covery and consumption platforms may point to a platform-
centric future, leaving RSS as a “second-class” distribution
mechanism reserved for amateurs. In this sense, the oligop-
olization of podcast discovery stands to reshape the medium
in its second decade.
Acknowledgements
The author would like to gratefully acknowledge the Faculty
Development and Scholarship Committee.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: The Provost
at Muhlenberg College provided funds to support this research.
ORCID iD
John L. Sullivan https://orcid.org/0000-0002-4676-1898
Notes
1. Platforms are “open” in the sense that they enable users to eas-
ily create, store, and distribute media content with little to no
technical knowledge. This “openness” is bounded in important
ways, however. For example, users typically have little control
over the storage or modification of their data. In addition, the
algorithms that make their content available to other platform
users are often opaque at best.
2. This is not the case for two large storage platforms that offer
free accounts, SoundCloud and Anchor.fm. In addition,
Blogger.com and Wordpress.com have typically offered free
hosting services for blogs.
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Author Biography
John L. Sullivan (PhD/MA University of Pennsylvania) is professor
of Media and Communication at Muhlenberg College in Allentown,
PA. His research interests include the political economy of cultural
production, media industries, and podcasting.
... Zira; Facebook, Twitter ve Instagram gibi sosyal ağlara Netflix, Spotify gibi yeni ücretli içerik sağlayıcılar eklenirken YouTube gibi platformalar güç kazanarak ön plana çıkmıştır. Ancak Podcast yayıncılığının en önemli kilometre taşlarından birisi, iTunes üzerinden erişilen bir milyarlık abone sayısıdır (Sullivan, 2019). Sadece bir platform üzerinden ve on yıldan daha kısa bir sürede böylesi bir abonelik (subscription) sayısına erişilmesi altı çizilmesi gereken bir noktadır. ...
... Podcastler daha çok rahatlamak ve dinlenmek için tercih edilirken, yayınlara ulaşım kanalı olarak Spotify kullanılmaktadır. Bu bakımdan podcastlerin yaygınlaşmasında önemli bir etkisi olan iTunes (Sullivan, 2019), Spotify platformunun gerisinde kalmaktadır. Ayrıca katılımcılar; duyguları etkilediği, dikkati üzerine çektiği ve hatırda kalınmayı artırdığı için müzik kullanımı yüksek bir katılımla desteklemişlerdir ve kullanılması gerektiğini iletmişlerdir. ...
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... Jika kita amati keunggulan pada Youtube dan Podcast adanya interaksi dalam bentuk percakapan, interaksi bersifat visual, dan audio. Seperti yang dikemukakan oleh (Sullivan, 2019) bahwa podcasting berkembang pesat sebagai fenomena budaya populer yang dapat menghubungkan pendengar dengan konten audio yang dibuat oleh para profesional, stasiun radio, dan para penghobi amatir. ...
... In the colloquial sense, radio may not be associated with new media, but the internet-radio convergence that has been going on for the past 30 years has made radio a place of great interest to new media researchers (Bonini, 2022;Bottomley, 2020). One growing area of research is podcasts (Bottomley, 2015;Chan-Olmsted and Wang, 2022;Lindgren and Loviglio, 2022), especially in the context of the development of streaming platforms (Sullivan, 2019). Another fledgling area of research is the use of new funding methods, such as crowdfunding, to develop podcasts and radio stations (Fernández Sande, 2014;Fernández Sande and Gallego Pérez, 2015;Galuszka and Chmielewski, 2023;Rei-Anderson, 2022). ...
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Lists and Social Media Lists have long been an ordering mechanism for computer-mediated social interaction. While far from being the first such mechanism, blogrolls offered an opportunity for bloggers to provide a list of their peers; the present generation of social media environments similarly provide lists of friends and followers. Where blogrolls and other earlier lists may have been user-generated, the social media lists of today are more likely to have been produced by the platforms themselves, and are of intrinsic value to the platform providers at least as much as to the users themselves; both Facebook and Twitter have highlighted the importance of their respective “social graphs” (their databases of user connections) as fundamental elements of their fledgling business models. This represents what Mejias describes as “nodocentrism,” which “renders all human interaction in terms of network dynamics (not just any network, but a digital network with a profit-driven infrastructure).” The communicative content of social media spaces is also frequently rendered in the form of lists. Famously, blogs are defined in the first place by their reverse-chronological listing of posts (Walker Rettberg), but the same is true for current social media platforms: Twitter, Facebook, and other social media platforms are inherently centred around an infinite, constantly updated and extended list of posts made by individual users and their connections. The concept of the list implies a certain degree of order, and the orderliness of content lists as provided through the latest generation of centralised social media platforms has also led to the development of more comprehensive and powerful, commercial as well as scholarly, research approaches to the study of social media. Using the example of Twitter, this article discusses the challenges of such “big data” research as it draws on the content lists provided by proprietary social media platforms. Twitter Archives for Research Twitter is a particularly useful source of social media data: using the Twitter API (the Application Programming Interface, which provides structured access to communication data in standardised formats) it is possible, with a little effort and sufficient technical resources, for researchers to gather very large archives of public tweets concerned with a particular topic, theme or event. Essentially, the API delivers very long lists of hundreds, thousands, or millions of tweets, and metadata about those tweets; such data can then be sliced, diced and visualised in a wide range of ways, in order to understand the dynamics of social media communication. Such research is frequently oriented around pre-existing research questions, but is typically conducted at unprecedented scale . The projects of media and communication researchers such as Papacharissi and de Fatima Oliveira, Wood and Baughman, or Lotan, et al.—to name just a handful of recent examples—rely fundamentally on Twitter datasets which now routinely comprise millions of tweets and associated metadata, collected according to a wide range of criteria. What is common to all such cases, however, is the need to make new methodological choices in the processing and analysis of such large datasets on mediated social interaction. Our own work is broadly concerned with understanding the role of social media in the contemporary media ecology, with a focus on the formation and dynamics of interest- and issues-based publics. We have mined and analysed large archives of Twitter data to understand contemporary crisis communication (Bruns et al), the role of social media in elections (Burgess and Bruns), and the nature of contemporary audience engagement with television entertainment and news media (Harrington, Highfield, and Bruns). Using a custom installation of the open source Twitter archiving tool yourTwapperkeeper , we capture and archive all the available tweets (and their associated metadata) containing a specified keyword (like “Olympics” or “dubstep”), name (Gillard, Bieber, Obama) or hashtag (#ausvotes, #royalwedding, #qldfloods). In their simplest form, such Twitter archives are commonly stored as delimited (e.g. comma- or tab-separated) text files, with each of the following values in a separate column: text: contents of the tweet itself, in 140 characters or less to_user_id: numerical ID of the tweet recipient (for @replies) from_user: screen name of the tweet sender id: numerical ID of the tweet itself from_user_id: numerical ID of the tweet sender iso_language_code: code (e.g. en, de, fr, ...) of the sender’s default language source: client software used to tweet (e.g. Web, Tweetdeck, ...) profile_image_url: URL of the tweet sender’s profile picture geo_type: format of the sender’s geographical coordinates geo_coordinates_0: first element of the geographical coordinates geo_coordinates_1: second element of the geographical coordinates created_at: tweet timestamp in human-readable format time: tweet timestamp as a numerical Unix timestamp In order to process the data, we typically run a number of our own scripts (written in the programming language Gawk ) which manipulate or filter the records in various ways, and apply a series of temporal, qualitative and categorical metrics to the data, enabling us to discern patterns of activity over time, as well as to identify topics and themes, key actors, and the relations among them; in some circumstances we may also undertake further processes of filtering and close textual analysis of the content of the tweets. Network analysis (of the relationships among actors in a discussion; or among key themes) is undertaken using the open source application Gephi . While a detailed methodological discussion is beyond the scope of this article, further details and examples of our methods and tools for data analysis and visualisation, including copies of our Gawk scripts, are available on our comprehensive project website, Mapping Online Publics . In this article, we reflect on the technical, epistemological and political challenges of such uses of large-scale Twitter archives within media and communication studies research, positioning this work in the context of the phenomenon that Lev Manovich has called “big social data.” In doing so, we recognise that our empirical work on Twitter is concerned with a complex research site that is itself shaped by a complex range of human and non-human actors, within a dynamic, indeed volatile media ecology (Fuller), and using data collection and analysis methods that are in themselves deeply embedded in this ecology. “Big Social Data” As Manovich’s term implies, the Big Data paradigm has recently arrived in media, communication and cultural studies—significantly later than it did in the hard sciences, in more traditionally computational branches of social science, and perhaps even in the first wave of digital humanities research (which largely applied computational methods to pre-existing, historical “big data” corpora)—and this shift has been provoked in large part by the dramatic quantitative growth and apparently increased cultural importance of social media—hence, “big social data.” As Manovich puts it: For the first time, we can follow [the] imaginations, opinions, ideas, and feelings of hundreds of millions of people. We can see the images and the videos they create and comment on, monitor the conversations they are engaged in, read their blog posts and tweets, navigate their maps, listen to their track lists, and follow their trajectories in physical space. (Manovich 461) This moment has arrived in media, communication and cultural studies because of the increased scale of social media participation and the textual traces that this participation leaves behind—allowing researchers, equipped with digital tools and methods, to “study social and cultural processes and dynamics in new ways” (Manovich 461). However, and crucially for our purposes in this article, many of these scholarly possibilities would remain latent if it were not for the widespread availability of Open APIs for social software (including social media) platforms. APIs are technical specifications of how one software application should access another, thereby allowing the embedding or cross-publishing of social content across Websites (so that your tweets can appear in your Facebook timeline, for example), or allowing third-party developers to build additional applications on social media platforms (like the Twitter user ranking service Klout ), while also allowing platform owners to impose de facto regulation on such third-party uses via the same code. While platform providers do not necessarily have scholarship in mind, the data access affordances of APIs are also available for research purposes. As Manovich notes, until very recently almost all truly “big data” approaches to social media research had been undertaken by computer scientists (464). But as part of a broader “computational turn” in the digital humanities (Berry), and because of the increased availability to non-specialists of data access and analysis tools, media, communication and cultural studies scholars are beginning to catch up. Many of the new, large-scale research projects examining the societal uses and impacts of social media—including our own—which have been initiated by various media, communication, and cultural studies research leaders around the world have begun their work by taking stock of, and often substantially extending through new development, the range of available tools and methods for data analysis. The research infrastructure developed by such projects, therefore, now reflects their own disciplinary backgrounds at least as much as it does the fundamental principles of computer science. In turn, such new and often experimental tools and methods necessarily also provoke new epistemological and methodological challenges. The Twitter API and Twitter Archives The Open API was a key aspect of mid-2000s ideas about the value of the open Web and “Web 2.0” business models (O’Reilly), emphasising the open, cross-platform sharing of content as well as promoting innovation at the margins via third-party application development—and it was in this ideological environment that the microblogging service Twitter launched and experienced rapid growth in popularity among users and developers alike. As José van Dijck cogently argues, however, a complex interplay of technical, economic and social dynamics has seen Twitter shift from a relatively open, ad hoc and user-centred platform toward a more formalised media business: For Twitter, the shift from being primarily a conversational communication tool to being a global, ad-supported followers tool took place in a relatively short time span. This shift did not simply result from the owner’s choice for a distinct business model or from the company’s decision to change hardware features. Instead, the proliferation of Twitter as a tool has been a complex process in which technological adjustments are intricately intertwined with changes in user base, transformations of content and choices for revenue models. (van Dijck 343) The specifications of Twitter’s API, as well as the written guidelines for its use by developers (Twitter, “Developer Rules”) are an excellent example of these “technological adjustments” and the ways they are deeply interwined with Twitter’s search for a viable revenue model. These changes show how the apparent semantic openness or “interpretive flexibility” of the term “platform” allows its meaning to be reshaped over time as the business models of platform owners change (Gillespie). The release of the API was first announced on the Twitter blog in September 2006 (Stone), not long after the service’s launch but after some popular third-party applications (like a mashup of Twitter with Google Maps creating a dynamic display of recently posted tweets around the world) had already been developed. Since then Twitter has seen a flourishing of what the company itself referred to as the “Twitter ecosystem” (Twitter, “Developer Rules”), including third-party developed client software (like Twitterific and TweetDeck), institutional use cases (such as large-scale social media visualisations of the London Riots in The Guardian ), and parasitic business models (including social media metrics services like HootSuite and Klout). While the history of Twitter’s API rules and related regulatory instruments (such as its Developer Rules of the Road and Terms of Use) has many twists and turns, there have been two particularly important recent controversies around data access and control. First, the company locked out developers and researchers from direct “firehose” (very high volume) access to the Twitter feed; this was accompanied by a crackdown on free and public Twitter archiving services like 140Kit and the Web version of Twapperkeeper (Sample), and coincided with the establishment of what was at the time a monopoly content licensing arrangement between Twitter and Gnip , a company which charges commercial rates for high-volume API access to tweets (and content from other social media platforms). A second wave of controversy among the developer community occurred in August 2012 in response to Twitter’s release of its latest API rules (Sippey), which introduce further, significant limits to API use and usability in certain circumstances. In essence, the result of these changes to the Twitter API rules, announced without meaningful consultation with the developer community which created the Twitter ecosystem, is a forced rebalancing of development activities: on the one hand, Twitter is explicitly seeking to “limit” (Sippey) the further development of API-based third-party tools which support “consumer engagement activities” (such as end-user clients), in order to boost the use of its own end-user interfaces; on the other hand, it aims to “encourage” the further development of “consumer analytics” and “business analytics” as well as “business engagement” tools. Implicit in these changes is a repositioning of Twitter users (increasingly as content consumers rather than active communicators), but also of commercial and academic researchers investigating the uses of Twitter (as providing a narrow range of existing Twitter “analytics” rather than engaging in a more comprehensive investigation both of how Twitter is used, and of how such uses continue to evolve). The changes represent an attempt by the company to cement a certain, commercially viable and valuable, vision of how Twitter should be used (and analysed), and to prevent or at least delay further evolution beyond this desired stage. Although such attempts to “freeze” development may well be in vain, given the considerable, documented role which the Twitter user base has historically played in exploring new and unforeseen uses of Twitter (Bruns), it undermines scholarly research efforts to examine actual Twitter uses at least temporarily—meaning that researchers are increasingly forced to invest time and resources in finding workarounds for the new restrictions imposed by the Twitter API. Technical, Political, and Epistemological Issues In their recent article “Critical Questions for Big Data,” danah boyd and Kate Crawford have drawn our attention to the limitations, politics and ethics of big data approaches in the social sciences more broadly, but also touching on social media as a particularly prevalent site of social datamining. In response, we offer the following complementary points specifically related to data-driven Twitter research relying on archives of tweets gathered using the Twitter API. First, somewhat differently from most digital humanities (where researchers often begin with a large pre-existing textual corpus), in the case of Twitter research we have no access to an original set of texts—we can access only what Twitter’s proprietary and frequently changing API will provide. The tools Twitter researchers use rely on various combinations of parts of the Twitter API—or, more accurately, the various Twitter APIs (particularly the Search and Streaming APIs). As discussed above, of course, in providing an API, Twitter is driven not by scholarly concerns but by an attempt to serve a range of potentially value-generating end-users—particularly those with whom Twitter can create business-to-business relationships, as in their recent exclusive partnership with NBC in covering the 2012 London Olympics. The following section from Twitter’s own developer FAQ highlights the potential conflicts between the business-case usage scenarios under which the APIs are provided and the actual uses to which they are often put by academic researchers or other dataminers: Twitter’s search is optimized to serve relevant tweets to end-users in response to direct, non-recurring queries such as #hashtags, URLs, domains, and keywords. The Search API (which also powers Twitter’s search widget) is an interface to this search engine. Our search service is not meant to be an exhaustive archive of public tweets and not all tweets are indexed or returned. Some results are refined to better combat spam and increase relevance. Due to capacity constraints, the index currently only covers about a week’s worth of tweets. (Twitter, “Frequently Asked Questions”) Because external researchers do not have access to the full, “raw” data, against which we could compare the retrieved archives which we use in our later analyses, and because our data access regimes rely so heavily on Twitter’s APIs—each with its technical quirks and limitations—it is impossible for us to say with any certainty that we are capturing a complete archive or even a “representative” sample (whatever “representative” might mean in a data-driven, textualist paradigm). In other words, the “lists” of tweets delivered to us on the basis of a keyword search are not necessarily complete; and there is no way of knowing how incomplete they are. The total yield of even the most robust capture system (using the Streaming API and not relying only on Search) depends on a number of variables: rate limiting, the filtering and spam-limiting functions of Twitter’s search algorithm, server outages and so on; further, because Twitter prohibits the sharing of data sets it is difficult to compare notes with other research teams. In terms of epistemology, too, the primary reliance on large datasets produces a new mode of scholarship in media, communication and cultural studies: what emerges is a form of data-driven research which tends towards abductive reasoning; in doing so, it highlights tensions between the traditional research questions in discourse or text-based disciplines like media and communication studies, and the assumptions and modes of pattern recognition that are required when working from the “inside out” of a corpus, rather than from the outside in (for an extended discussion of these epistemological issues in the digital humanities more generally, see Dixon). Finally, even the heuristics of our analyses of Twitter datasets are mediated by the API: the datapoints that are hardwired into the data naturally become the most salient, further shaping the type of analysis that can be done. For example, a common process in our research is to use the syntax of tweets to categorise it as one of the following types of activity: original tweets: tweets which are neither @reply nor retweet retweets: tweets which contain RT @ user … (or similar) unedited retweets: retweets which start with RT @ user … edited retweets: retweets do not start with RT @ user … genuine @replies: tweets which contain @ user , but are not retweets URL sharing: tweets which contain URLs (Retweets which are made using the Twitter “retweet button,” resulting in verbatim passing-along without the RT @ user syntax or an opportunity to add further comment during the retweet process, form yet another category, which cannot be tracked particularly effectively using the Twitter API.) These categories are driven by the textual and technical markers of specific kinds of interactions that are built into the syntax of Twitter itself (@replies or @mentions, RTs); and specific modes of referentiality (URLs). All of them focus on (and thereby tend to privilege) more informational modes of communication, rather than the ephemeral, affective, or ambiently intimate uses of Twitter that can be illuminated more easily using ethnographic approaches: approaches that can actually focus on the individual user, their social contexts, and the broader cultural context of the traces they leave on Twitter. Conclusions In this article we have described and reflected on some of the sociotechnical, political and economic aspects of the lists of tweets—the structured Twitter data upon which our research relies—which may be gathered using the Twitter API. As we have argued elsewhere (Bruns and Burgess)—and, hopefully, have begun to demonstrate in this paper—media and communication studies scholars who are actually engaged in using computational methods are well-positioned to contribute to both the methodological advances we highlight at the beginning of this paper and the political debates around computational methods in the “big social data” moment on which the discussion in the second part of the paper focusses. One pressing issue in the area of methodology is to build on current advances to bring together large-scale datamining approaches with ethnographic and other qualitative approaches, especially including close textual analysis. More broadly, in engaging with the “big social data” moment there is a pressing need for the development of code literacy in media, communication and cultural studies. In the first place, such literacy has important instrumental uses: as Manovich argues, much big data research in the humanities requires costly and time-consuming (and sometimes alienating) partnerships with technical experts (typically, computer scientists), because the free tools available to non-programmers are still limited in utility in comparison to what can be achieved using raw data and original code (Manovich, 472). But code literacy is also a requirement of scholarly rigour in the context of what David Berry calls the “computational turn,” representing a “third wave” of Digital Humanities. Berry suggests code and software might increasingly become in themselves objects of, and not only tools for, research: I suggest that we introduce a humanistic approach to the subject of computer code, paying attention to the wider aspects of code and software, and connecting them to the materiality of this growing digital world. With this in mind, the question of code becomes increasingly important for understanding in the digital humanities, and serves as a condition of possibility for the many new computational forms that mediate our experience of contemporary culture and society. (Berry 17) A first step here lies in developing a more robust working knowledge of the conceptual models and methodological priorities assumed by the workings of both the tools and the sources we use for “big social data” research. Understanding how something like the Twitter API mediates the cultures of use of the platform, as well as reflexively engaging with its mediating role in data-driven Twitter research, promotes a much more materialist critical understanding of the politics of the social media platforms (Gillespie) that are now such powerful actors in the media ecology. References Berry, David M. “Introduction: Understanding Digital Humanities.” Understanding Digital Humanities . Ed. David M. Berry. 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