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.

Managing the constant flow of incoming messages is a daily challenge faced by knowledge workers who use technologies such as e‐mail and other digital communication tools. This study focuses on the most ubiquitous of these technologies, e‐mail, and unobtrusively explores the ongoing inbox‐management activities of thousands of users worldwide over a period of 8 months. The study describes the dynamics of these inboxes throughout the day and the week as users strive to handle incoming messages, read them, classify them, respond to them in a timely manner, and archive them for future reference, all while carrying out the daily tasks of knowledge workers. It then tests several hypotheses about the influence of specific inbox‐management behaviors in mitigating the causes of e‐mail overload, and proposes a continuous index that quantifies one of these inbox‐management behaviors. This inbox clearing index (ICI) expands on the widely cited trichotomous classification of users into frequent filers, spring cleaners, and no filers, as suggested by Whittaker and Sidner (1996). We propose that the ICI allows shifting the focus, from classifying users to characterizing a diversity of user behaviors and measuring the relationships between these behaviors and desired outcomes.

Lately there is great progress in business organizations perception towards social aspects. Competitive organizations need to create innovation and segregate in the market. Business interactions help reaching those goals but finding the effective interactions is a challenge. We propose a prediction method, based on social networks analysis (SNA) and text data mining (TDM), for predicting which nodes in a social network will be linked next. The network which is used to demonstrate the proposed prediction method is composed of academic co-authors who collaborated on writing articles. Without loss of generality, the academic co-authoring network demonstrates the proposed prediction procedure due to its similarity to other networks, such as business co-operation networks. The results show that the best prediction is achieved by incorporating TDM with SNA.

Online communities are described in terms of collections of virtual neighborhoods, each of which is a subset of interdependent members. The significant virtual neighborhoods are revealed by fitting parametric Markov field models (p*) to the response relations of the communities. The underlying theoretical mechanisms are then deduced by matching the revealed virtual neighborhoods with the predictions of network emergence theories. We demonstrate that the underlying mechanisms are related to specific design features of the communities. This method can be extended to other relations in online communities and to longitudinal analysis, and applied to real-time monitoring of online communications.

We provide empirical support for the assertions that high level of knowledge construction is associated with structured design and that knowledge construction is associated with cohesion and equivalence network structures. We built and analyzed two CSCL communities - one structured the other nonstructured. The levels of learning processes were measured by content analysis. The social capital structure of the communities was analyzed by social network analysis. The analysis revealed that the structured community developed social capital, encoded by a mesh of interlinked cliques, and that participants undertook bridging and triggering roles, and exhibited high levels of constructing knowledge. The tutor (guide) remained on the side. The nonstructured community did not construct knowledge, cohesion was dull, and participants did not undertake any essential roles.