Member behavior in dynamic online communities: Role affiliation frequency model

People's social life has become more embedded in dynamic online communities. Each online community can be viewed as a temporal online social network (OSN). The interaction level among OSN members leads to the emergence of dynamic social roles, which change and evolve over time, creating a sequence of temporal roles. These role sequences show diversity in the role-affiliation frequency of members. That diversity enables modeling the dynamic behaviors of individuals. This paper proposes a temporal role-affiliation frequency model (RAFM) which detects the time evolving roles of each member and analyzes her/his role-affiliation frequency to infer her/his latent behavior. Applying the RAFM to real interaction data, collected in four online communities, revealed the identity of influential members. In addition, members with similar temporal behavioral patterns were found to have similar latent behavior patterns. These patterns are manifested via similar role transitions in different OSNs whose temporal interaction rhythms were compatible. These two research findings contribute to OSN research and knowledge via improved understanding of member behavior online based on role-affiliation frequency and role transitions. Thus, member latent behavior can be inferred, and influential members can be identified.