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Millions of users on the Internet discuss a variety of topics on Question-and-Answer (Q8A) 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 Q8A 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 a user activity-based time series from 50 randomly selected Q8A 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 Q8A instances by the previously identified user activity profiles, we classify those 50 Q8A instances into three different activity profiles. Our parsimonious categorization of Q8A 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 Q8A instances, but we also derive practical implications for optimizing Q8A community building efforts, as we, e.g., recommend which user types to focus on at different developmental stages of a Q8A community.

AbstractSpectator periodicals contributed to spreading the ideas of the Age of Enlightenment, a turning point in human history and the foundation of our modern societies. In this work, we study the spirit and atmosphere captured in the spectator periodicals about important social issues from the 18thcentury by analyzing text sentiment of those periodicals. Specifically, based on a manually annotated corpus of over 3 700 issues published in five different languages and over a period of more than one hundred years, we conduct a three-fold sentiment analysis: First, we analyze the development of sentiment over time as well as the influence of topics and narrative forms on sentiment. Second, we construct sentiment networks to assess the polarity of perceptions between different entities, including periodicals, places and people. Third, we construct and analyze sentiment word networks to determine topological differences between words with positive and negative polarity allowing us to make conclusions on how sentiment was expressed in spectator periodicals.Our results depict a mildly positive tone in spectator periodicals underlining the positive attitude towards important topics of the Age of Enlightenment, but also signaling stylistic devices to disguise critique in order to avoid censorship. We also observe strong regional variation in sentiment, indicating cultural and historic differences between countries. For example, while Italy perceived other European countries as positive role models, French periodicals were frequently more critical towards other European countries. Finally, our topological analysis depicts a weak overrepresentation of positive sentiment words corroborating our findings about a general mildly positive tone in spectator periodicals.We believe that our work based on the combination of the sentiment analysis of spectator periodicals and the extensive knowledge available from literary studies sheds interesting new light on these publications. Furthermore, we demonstrate the inclusion of sentiment analysis as another useful method in the digital humanist's distant reading toolbox.