  Studying misinformation and how to deal with unhealthy behaviours within
online discussions has recently become an important field of research within
social studies. With the rapid development of social media, and the increasing
amount of available information and sources, rigorous manual analysis of such
discourses has become unfeasible. Many approaches tackle the issue by studying
the semantic and syntactic properties of discussions following a supervised
approach, for example using natural language processing on a dataset labeled
for abusive, fake or bot-generated content. Solutions based on the existence of
a ground truth are limited to those domains which may have ground truth.
However, within the context of misinformation, it may be difficult or even
impossible to assign labels to instances. In this context, we consider the use
of temporal dynamic patterns as an indicator of discussion health. Working in a
domain for which ground truth was unavailable at the time (early COVID-19
pandemic discussions) we explore the characterization of discussions based on
the the volume and time of contributions. First we explore the types of
discussions in an unsupervised manner, and then characterize these types using
the concept of ephemerality, which we formalize. In the end, we discuss the
potential use of our ephemerality definition for labeling online discourses
based on how desirable, healthy and constructive they are.
