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.
@cite_4 demonstrated the potential of mixed methods of analysis for investigating sequence of editing behavior in online knowledge collaborations. They review the literature on online knowledge collaboration and sequence analysis and provide a framework for analysis of sequences in online communities. They argue that analyzing log data using established methods in natural language processing and bioinformatics can provide insights that can answer various kinds of research questions inspired by dynamics of online communities. Moreover, they provide a use case of their framework by analyzing sequence of editing behavior in Wikipedia. They analyze the prevalence and significance of different sequence of editing patterns and empirically assign semantic intuitions to various observed patterns. They capture the strength of sequence methods in analyzing behavioral data and provide a comprehensive framework for formulating similar problems. Unlike my work, they don't specifically consider temporal differences between different events and encode the interval between events by including the temporal data in representation of events. They don't provide a further investigation of how users with different patterns of behavior can be similar or different in the behavior space.
@cite_4
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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.