Categorising the online communities of stack exchange using quantitative user behaviour features
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Aumayr, Erik. (2016). Categorising the online communities of stack exchange using quantitative user behaviour features (pp. 18)
Maintaining online communities is vital in order to increase and retain their economic and social value. Before applying any performance altering strategies, it is important to determine the different types of communities, as they might be affected differently. In the literature, we find qualitative categories such as transactional and interest-based. However, these qualitative classification approaches do not guarantee to reflect the underlying user behaviour. Yet it is crucial to study the user behaviour, e.g. how many users join per day, in order to understand which communities perform well and which ones require intervention by a community manager. In this work, we present a bottomup community clustering approach that relies on quantitatively measurable user behaviour features. We examine 29 online communities of the Stack Exchange platform, and describe the extracted features that capture the user behaviour. Based on these features we then categorise the communities. By analysing the clusters, we find that they correspond to a certain degree to intuitive topical themes.