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Millions of users on the Internet discuss a variety of topics onQuestion-and-Answer (Q&A) instances. However, not all instances and topicsreceive the same amount of attention, as some thrive and achieveself-sustaining levels of activity, while others fail to attract users andeither never grow beyond being a small niche community or become inactive.Hence, it is imperative to not only better understand but also to distilldeciding factors and rules that define and govern sustainable Q&A instances. Weaim to empower community managers with quantitative methods for them to betterunderstand, control and foster their communities, and thus contribute to makingthe Web a more efficient place to exchange information. To that end, weextract, model and cluster user activity-based time series from $50$ randomlyselected Q&A instances from the Stack Exchange network to characterize userbehavior. We find four distinct types of user activity temporal patterns, whichvary primarily according to the users' activity frequency. Finally, by breakingdown total activity in our 50 Q&A instances by the previously identified useractivity profiles, we classify those 50 Q&A instances into three differentactivity profiles. Our parsimonious categorization of Q&A instances aligns withthe stage of development and maturity of the underlying communities, and canpotentially help operators of such instances: We not only quantitatively assessprogress of Q&A instances, but we also derive practical implications foroptimizing Q&A community building efforts, as we e.g. recommend which usertypes to focus on at different developmental stages of a Q&A community.

Activity Archetypes in Question-and-Answer (Q&A) Websites - A Study of 50 Stack Exchange Instances