Hi hello my name is Tanvi Bajpai and I'll be talking about MIC - an affordance aware
framework for platform moderation. So, when we think about moderating online communities the
online communities we usually think about are subreddits or Facebook groups. In reality,
an online community can be anything as small as the handful of users that are
subscribed to a University mailing list to as large as platforms in their entirety.
Regardless of the size or scope of an online community the moderation strategies
and challenges these communities will use and face will be highly informed by the features
of the platforms that they are situated on. We refer to the aspects of platforms
that impact moderation as that platform's moderation ecosystem. Understanding and
analyzing a platform's moderation ecosystem is important for all stakeholders of moderation.
For example, community founders or moderators that are interested in using -- or already
use -- Discord might want to know what affordances or features the platform has that could affect how
they moderate or run their community. So, these are some examples. For moderation researchers,
understanding individual platforms' ecosystems can help answer broader questions about moderation at
large. For instance by studying moderation in voice channels on Discord we could better
understand how real-time, ephemeral audio is to be moderated or what challenges might
arise in this modality that don't arise in text based platforms like Reddit.
Clearly there's a great deal of value in studying the moderation ecosystems of individual platforms.
However over the last decade we've seen a drastic increase in the rate at which
platforms are being created and updated. As a result, the moderation ecosystems of
platforms are constantly changing often rendering moderation research on these platforms outdated,
sometimes even before that research can get published. Similarly, we can no longer rely
on one or two representative platforms to draw conclusions about moderation at large.
By that I mean, maybe five years ago Discord might have been considered an adequate representative
for understanding real-time ephemeral social audio moderation. However, in just the last two years
there have been several new live audio social platforms that have been developed
as well as audio feature additions to several other existing platforms.
Although these new platforms, these new features all have to do with live social audio,
they each offer diverse and distinct affordances that could impact how moderation is carried out on
each platform and the moderation challenges that users could face on each platform.
In light of all this, we believe that the moderation research community needs to
have a unifying language with which to discuss moderation that highlights platforms moderation
related affordances. Furthermore, the way in which we understand these moderation ecosystems should
be easily updatable, and should allow us to see similarities and differences between platforms
that affect moderation clearly. To address these needs we have developed the MIC framework for
understanding moderation on platforms. The MIC framework builds upon Grimmelmann's taxonomy of
moderation, which provides a well-established vocabulary for moderation on online communities
that is not platform specific. In the MIC framework, we describe 12 platform specific
affordances that are known to play a role in moderation (and we know this is the case because
they they pop up in the breadth of moderation research).These affordances fall into three
categories which are named for the components of an online community as per Grimmelmann's taxonomy.
Grimmelmann states that an online community is made up of Members, the Infrastructure that
the members use, and the Content that that they share. MIC allows us to represent the
moderation ecosystems of platforms using these 12 affordances. Here we have what we call a MIC
diagram for Discord. This diagram displays all 12 affordances clearly. Our framework also has
a notion of inter-affordance relationships, since each of these affordances don't exist in a vacuum
but in an ecosystem. These relationships are reflected in the arrows. For example,
we have a bi-directional arrow between the user roles on Discord and the access and permissions
of Discord because those two affordances are typically tied together: certain user roles have
certain access and permission settings associated with them. To demonstrate how the mic framework
can be used in practice we conducted two case studies. In these case studies we look at three
popular audio-based social platforms: Spotify, Discord, and Clubhouse. In our first case study,
we use MIC to analyze the moderation ecosystem of Clubhouse, which is an app that launched in
April 2020 and became very popular because of the pandemic. Here we have a MIC diagram that shows
us what the platform affordances and relationships were as of June of 2021. That's a little over a
year of Clubhouse being around. Ironically, June of 2021 is when we first submitted this paper.
Unsurprisingly Clubhouse went through several updates over the following months -- updates
that spanned the time between when our paper was submitted, then revised,
and then eventually accepted. Here we have a MIC diagram that reflects Clubhouse's moderation
ecosystem as of January 2022, six months after the previous diagram. As you can see several
affordances were changed as a result of this these updates and because we had observed certain
relationships between Clubhouse's affordances we were able to propagate updates across these
relationships. For instance we had observed a relationship between the modalities on clubhouse
and their inter-platform dependencies. When Clubhouse updated the modalities that it offered
we re-evaluated what clubhouse's inter-platform dependencies were, since a change to modalities
would prompt a change to inter-platform dependencies.
In our second case study, we use MIC to compare and contrast three platforms to look
for similarities and differences across their affordances and inter-affordance
relationships. Here are the MIC diagrams for Discord and Spotify. By making MIC-guided
comparisons and identifying similarities and differences between platforms we can uncover
where platforms can adapt and learn from each other and adapt and learn from more established
better studied platforms. For instance, Spotify has been around since 2006, so its been around for a long time,
and while it's not incredibly similar to platforms such as Clubhouse and Discord there's still
aspects of Spotify that Clubhouse could borrow from since they both have non-ephemeral audio.
Another example is that Discord and Clubhouse are pretty similar in that there are users that
are moderators. But, Discord offers its users a suite of moderation tools as well as access
to an API with which they can build their own tools for moderation. Clubhouse doesn't
offer anything like this and so it might be beneficial for a Clubhouse to look into
offering tools such as the one ones found on Discord since those tools are so successful.
In summary the MIC framework we've developed allows moderation stakeholders to construct simple
yet explicit representations of complex platform moderation ecosystems. It also allows us to update
these representations to reflect how platform changes will impact the moderation ecosystem
at large. And finally, we can use MIC to compare and contrast moderation
ecosystems of multiple platforms and identify potential moderation solutions or challenges
that these platforms might face based on their similarities and differences
to other well-studied platforms. You can find more details about the MIC framework,
our case studies, and further implications in our paper. Thank you so much for watching!