The degree to which individuals can exert influence on propagation of information and opinion dynamics in online communities is highly dependent on their social status. Therefore, there is a high demand for identifying influential users in a community by predicting their social position in that community. Moreover, understanding how people with various social status behave, can shed light on the dynamics of interaction in social networks. In this paper, I study an evolving online social network originated from an online community for university students and I tackle the problem of forecasting users' social status, represented as their PageRank, based on frequency of recurring temporal sequences of observed behavior, i.e. behavioral motifs. I show that individuals with different values of PageRank exhibit different behavior even in early weeks since the online community's inception and it is possible to forecast future PageRank values given frequency of behavioral motifs with high accuracy.
Research into socio-technical systems like Wikipedia has overlooked important structural patterns in the coordination of distributed work. This paper argues for a conceptual reorientation towards sequences as a fundamental unit of analysis for understanding work routines in online knowledge collaboration. We outline a research agenda for researchers in computer-supported cooperative work (CSCW) to understand the relationships, patterns, antecedents, and consequences of sequential behavior using methods already developed in fields like bio-informatics. Using a data set of 37,515 revisions from 16,616 unique editors to 96 Wikipedia articles as a case study, we analyze the prevalence and significance of different sequences of editing patterns. We illustrate the mixed method potential of sequence approaches by interpreting the frequent patterns as general classes of behavioral motifs. We conclude by discussing the methodological opportunities for using sequence analysis for expanding existing approaches to analyzing and theorizing about co-production routines in online knowledge collaboration.