  Millions of users on the Internet discuss a variety of topics on
Question-and-Answer (Q&A) instances. However, not all instances and topics
receive the same amount of attention, as some thrive and achieve
self-sustaining levels of activity, while others fail to attract users and
either never grow beyond being a small niche community or become inactive.
Hence, it is imperative to not only better understand but also to distill
deciding factors and rules that define and govern sustainable Q&A instances. We
aim to empower community managers with quantitative methods for them to better
understand, control and foster their communities, and thus contribute to making
the Web a more efficient place to exchange information. To that end, we
extract, model and cluster user activity-based time series from $50$ randomly
selected Q&A instances from the Stack Exchange network to characterize user
behavior. We find four distinct types of user activity temporal patterns, which
vary primarily according to the users' activity frequency. Finally, by breaking
down total activity in our 50 Q&A instances by the previously identified user
activity profiles, we classify those 50 Q&A instances into three different
activity profiles. Our parsimonious categorization of Q&A instances aligns with
the stage of development and maturity of the underlying communities, and can
potentially help operators of such instances: We not only quantitatively assess
progress of Q&A instances, but we also derive practical implications for
optimizing Q&A community building efforts, as we e.g. recommend which user
types to focus on at different developmental stages of a Q&A community.
