  Increasingly taking place in online spaces, modern political conversations
are typically perceived to be unproductively affirming -- siloed in so called
``echo chambers'' of exclusively like-minded discussants. Yet, to date we lack
sufficient means to measure viewpoint diversity in conversations. To this end,
in this paper, we operationalize two viewpoint metrics proposed for recommender
systems and adapt them to the context of social media conversations. This is
the first study to apply these two metrics (Representation and Fragmentation)
to real world data and to consider the implications for online conversations
specifically. We apply these measures to two topics -- daylight savings time
(DST), which serves as a control, and the more politically polarized topic of
immigration. We find that the diversity scores for both Fragmentation and
Representation are lower for immigration than for DST. Further, we find that
while pro-immigrant views receive consistent pushback on the platform,
anti-immigrant views largely operate within echo chambers. We observe less
severe yet similar patterns for DST. Taken together, Representation and
Fragmentation paint a meaningful and important new picture of viewpoint
diversity.
