Modeling the Diversity of User Behavior in Online Communities Our model raises a key question for future work: how the lognormal distributions in user ac- tivity and resolve interestingness arise. Lognormal distributions suggest underlying multiplicative processes are involved, but the specific mecha- nisms for these processes and how they depend on the web site design and type of content are not yet known. Identifying such causal mechanisms could benefit from controlled experiments, e.g., with randomly selected subgroups of users to avoid self-selection biases. The long-tail distributions observed in online communities pose a challenge for statistical modeling because samples may not be indicative of future behavior due to large variations among users. Thus these studies can benefit from robust statistical tools (Brown and Sethna, 2003) and require caution in situations where just a few highly active users can dominate the community. The study of online communities could benefit terest to small subgroups of users but not to the population as a whole. Automatically identifying such subgroups could help people find others with similar interests by supplementing comparisons based on ideological profiles. Our model of the networks describes the degree distribution but does not address other significant properties of the networks, such as community structure and assortativity. For a discussion of some of these points see Hogg et al. (2008). Nor does our model address detailed effects on user behavior due to their network neighbors. Another aspect of diversity, not included in our model, is the bursty time intervals between a user's successive activities on the site (Barabasi, 2005; Vazquez et al., 2006). One approach to understand- ing such behavior uses psychologically- motivated diffusion models of decision-making (Bogacz et al., 2006), which can describe this distribution and other aspects of users writing and commenting on