Most of virtual communities are built on the client/server system. There are some limitations on the client/server system such as the maintenance cost and the personal attribute protection. The peer-to-peer system has some strengths to overcome the limitations of client/server system. Therefore, we are willing to export the virtual community on the peer-to-peer system. There are two main team formation approaches in the current virtual community collaboration. Either one of these approaches alone has its limitations. In this study, we adopt the social network concept to design a team formation mechanism in order to overcome the limitations of current approaches. Besides, because of the natural of peer-to-peer system, the exchange of messages is sending and receiving on the network. The mechanism proposed in this research can also reduce the traffic cost of the team formation process. Furthermore, it maintains the fitness of members who are chosen in the same team.

With the exponential growth of Facebook users worldwide, this platform for social network online has become a powerful tool to connect individuals and share information with each other. This study explores the phenomenal trend of utilizing a Facebook application called Causes to help users organize into online communities for a specific cause and mobilize their resources for disaster relief during the Haiti earthquake disaster. Two separate samples of 100 joiners each from the American Red Cross (ARC) Cause on Facebook were randomly selected before and after the Haiti earthquake disaster to examine the differences of the composition (i.e., attributes) and structure (i.e., relational ties) of each social network. The social network analysis performed for this thesis research intends to fill the gap of historical research literature on recruitment to activism and support provision following a disaster in the digital age of the 21 st century. The results of this study show how understanding the membership size of online communities, salient identity for the cause through organizational affiliations, interpersonal ties among the joiners, density of the network as well as gender diversity can be crucial recruitment factors to leverage for disaster relief efforts. The findings reveal a beneficial partnership between disaster relief organizations and online social networks in mobilizing their resources for a speedy response to disasters.

From 1961 to 1971 the United States and the Republic of South Vietnam used chemicals to defoliate the coastal and upload forest areas of Viet Nam. The most notorious of these chemicals was named Agent Orange, a weaponized herbicide made up of two chemicals that, when combined, produced a toxic byproduct called TCDD-dioxin. Studied suggest that TCDD-dioxin causes significant human health problems in exposed American and Vietnamese veterans, and possibly their children (Agency, U.S. Environmental Protection, 2011). In the years since the end of the Vietnam War, volumes of discourse about Agent Orange has been generated, much of which is now digitally archived and machine-readable, providing rich sites of study ideal for “big data” text mining, extraction and computation. This study uses a combination of tools and text mining scripts developed in Python to study the descriptive phrases four discourse communities used across 45 years of discourse to talk about key issues in the debates over Agent Orange. Findings suggests these stakeholders describe and frame in significantly different ways, with Congress focused on taking action, the New York Times article and editorial corpus focused on controversy, and the Vietnamese News Agency focused on victimization. Findings also suggest that while new tools and methods make lighter work of mining large sets of corpora, a mixed-methods approach yields the most reliable insights. Though fully automated text analysis is still a distant reality, this method was designed to study potential effects of rhetoric on public policy and advocacy initiatives across large corpora of texts and spans of time.

Coalitions are widely associated with collective or collaborative attempts to influence organizational members, decisions, policies and events. Yet, surprisingly, relatively little is known about how coalitions develop within organizations. Employing an exploratory case study design and using social network analysis, the Rokeach Value Survey, and semi-structured interviews, this research demonstrated that it is possible to identify and study coalitions in a real organizational setting. I suggest that the inclusion and investigation of member relationships may advance the state of the art in organizational coalition research. A benefit of this study, and contrary to most coalition research, is that it used multiple forms of data, including demographic, historical, values-based and interaction patterns for work and social relationships.Two coalitions were identified in the organization studied. Formation centered on a single issue and each coalition followed a strategy designed to influence a possible change in structure and operation. Coalition members exhibited similarities across several factors, including tenure within the organization, education, race, age, and previous experiences. Analyses showed some similarity in member values within and between coalitions. The coalition attempting to maintain the current work structure demonstrated higher value similarity with non-coalition members. Social network analysis revealed that coalition members tended to be structurally similar to each other, more centrally located in the work network, and had higher correlation between coalition interactions and existing social relationships.

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Community, a notion transversal to all areas of Social Network Analysis, has drawn tremendous amount of attention across the sciences in the past decades. Numerous attempts to characterize both the sociological embodiment of the concept as well as its observable structural manifestation in the social network have to this date only converged in spirit. No formal consensus has been reached on the quantifiable aspects of community, despite it being deeply linked to topological and dynamic aspects of the underlying social network. Presenting a fresh approach to the evaluation of communities, this thesis introduces and builds upon the cohesion, a novel metric which captures the intrinsic quality, as a community, of a set of nodes in a network. The cohesion, defined in terms of social triads, was found to be highly correlated to the subjective perception of communitiness through the use of a large-scale online experiment in which users were able to compute and rate the quality of their social groups on Facebook. Adequately reflecting the complexity of social interactions, the problem of finding a maximally cohesive group inside a given social network is shown to be NP-hard. Using a heuristic approximation algorithm, applications of the cohesion to broadly different use cases are highlighted, ranging from its application to network visualization, to the study of the evolution of agreement groups in the United States Senate, to the understanding of the intertwinement between subjects' psychological traits and the cohesive structures in their social neighborhood. The use of the cohesion proves invaluable in that it offers non-trivial insights on the network structure and its relation to the associated semantic.

Establishing online communities of practice is an important part of the knowledge translation process in the modern healthcare system, but these online communities are new entity that is inherently different from traditional communities of practice that are dependent on existing social structures. The objective of this thesis is to combine communication analysis and content analysis to delve deeper into the communications within an online community to try and determine how online communities exist, and how that information can be leveraged to improve online knowledge translation. Using a novel approach this project will map the contents of online conversations to a structured medical lexicon (MeSH), and then use the inherent relationships of that lexicon to calculate term, user and thread similarities within an online community. These similarities, combined with connection analysis results, will provide a much deeper understanding of how online communities function. The methods developed here will then be tested on two separate mailing lists, the Pediatric Pain Mailing List (PPML) and SURGINET, a mailing list of general surgeons.

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No description available.

Certain properties of individuals’ social networks within their organizations are known to be associated with benefits. However, these properties are not universally beneficial for all individuals. To explain the differing utility of social connections for different actors, network research has tended to focus on factors relating to the actor’s characteristics, agency and cognition. With this dissertation, I explore a different contingency affecting actors’ abilities to leverage their networks: how observers perceive and evaluate the behavior of actors as they craft and use their networks, and how these attributions impact actors’ job performance. I develop a theoretical framework that incorporates social capital theory to develop a taxonomy of networking behaviors. I build upon network cognition research to explore how observers’ perceptions and attributions of actors’ networking behaviors rather than perceptions of network ties or structure affect actors’ outcomes. I draw upon attribution theory to suggest how observers’ attributions about actors may affect observers’ behavior towards actors, thus impacting actors’ outcomes. Results suggest that networking behaviors that are seen as serving the collective positively impact actors’ outcomes, while networking behaviors that are seen as self-serving negatively impact the actors’ outcomes by limiting access to high-status friends. However, attributions about an actor’s self-serving behavior augment the benefits the actor receives when he or she has access to high-status friends. When it comes to performance, networks matter, but it also matters how observers evaluate actors’ networking behaviors.