Relationship conflict is robustly linked to negative physiological responses that have serious implications for partners’ overall physical health. The link between relationship conflict and physiological reactivity, however, has been studied almost exclusively in a laboratory setting. The first aim of this study was to assess the link between conflict and physiological function in couples’ home environments. Newlywed spouses reported occurrences of marital conflict in a daily diary and concurrently provided morning and evening saliva samples for the calculation of daily diurnal cortisol slopes. Spouses experienced less steep (i.e., less healthy) diurnal cortisol slopes on days of greater marital conflict. The second aim of this study was to examine whether spouses’ connections with close others outside their marriages (i.e., quantity and quality of perceived network support) moderate physiological responses to marital conflict. Whereas the quantity of network support did not influence spouses’ responses to conflict, the quality of spouses’ network support attenuated the association between daily marital conflict and diurnal cortisol. Specifically, whereas those spouses who were less satisfied with their network support experienced less steep diurnal cortisol on days of greater marital conflict, those spouses who were more satisfied with their network support exhibited no effects of daily marital conflict on diurnal cortisol. Implications for maintaining quality social relationships outside a marriage are discussed. / text

This study’s focus was on individuals who are currently in romantic relationships or who were in one within the past six months. The goal of the study was to learn more about the relationship between the perceived difficulty of renewing a recently terminated relationship or terminating a current romantic relationship and social network closeness, integration, and levels of perceived support for the relationship from network members. The findings for this study support the prediction that an individual’s perceived difficulty of terminating an intact relationship was positively related to their levels of closeness with their partner’s social network members, the individual’s partner’s closeness with the individual’s social network members, and the perceived support for the relationship from network members. / text

The dissertation seeks to bridge the gap between privacy and social capital on SNS use by bringing the essential elements of social networking, privacy concerns, privacy management, self-disclosure, and social capital together to examine their complex relationships and the daily challenges every SNS user faces. The major purposes of this dissertation were to revisit the privacy paradox phenomenon, update the current relationships among privacy concerns, self-disclosure, and social capital on Facebook, integrate these relationships into a quantitative model, and explore the role of privacy management in these relationships. The goal was realized by using Amazon.com’s Mechanical Turk to test a theoretical model that used survey data from 522 respondents. The findings from the dissertation show the impact of the structural factor—Facebook social network intensity and diversity—and the impact of individuals’ self-disclosure on Facebook on their perceived bridging and bonding social capital. This dissertation employed various measurements of key variables to update the current status of the privacy paradox phenomenon—the disconnection between privacy concerns and self- disclosure on social media—and found the break of the traditional privacy paradox and the existence of the social privacy paradox. Findings also show that private information about personal information, thoughts, and ideas shared on Facebook become assets in using Facebook and accumulating social capital. Meanwhile, higher privacy concerns reduce the level of self-disclosure on Facebook. Therefore, privacy concerns become a barrier in Facebook use and in accumulating social capital within these networks. This dissertation further examined the mediating role of privacy management to solve the dilemma. Findings confirmed that privacy management is important in redirecting the relationships among privacy concerns, self-disclosure, and social capital. People who have higher privacy concerns tend to disclose fewer personal thoughts and ideas on Facebook and miss the opportunity to accumulate social capital. However, when they employ more privacy management strategies, they are more willing to self-disclose and thus accumulate more social capital on Facebook networks. Lastly, the proposed integrated model examined through SEM analysis confirms the delicate relationships among the social networking characteristics, privacy concerns, privacy management, self-disclosure, and social capital. / text

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.

Despite decades of effort in academia and industry, it is not yet possible to build machines that can replicate many seemingly-basic human perceptual abilities. This work focuses on the problem of face identification that most of us effortlessly solve daily. Substantial progress has been made towards the goal of automatically identifying faces under tightly controlled conditions; however, in the domain of unconstrained face images, many challenges remain. We observe that the recent combination of widespread digital photography, inexpensive digital storage and bandwidth, and online social networks has led to the sudden creation of repositories of billions of shared photographs and opened up an important new domain for unconstrained face identification research. Drawing upon the newly-popular phenomenon of “tagging,” we construct some of the first face identification datasets that are intended to model the digital social spheres of online social network members, and we examine various qualitative and quantitative properties of these image sets. The identification datasets we present here include up to 100 individuals, making them comparable to the average size of members’ networks of “friends” on a popular online social network, and each individual is represented by up to 100 face samples that feature significant real-world variation in appearance, expression, and pose. We demonstrate that biologically-inspired visual representations can achieve state-of-the-art face identification performance on our novel frontal and multi-pose face datasets. We also show that the addition of a tree-structured classifier and training set augmentation can enhance accuracy in the multi-pose setting. Finally, we illustrate that the machine-readable “social context” in which shared photos are often embedded can be applied to further boost face identification accuracy. Taken together, our results suggest that accurate automated face identification in vast online shared photo collections is now feasible. / Engineering and Applied Sciences

Abstract As online social network services are becoming one of the dominant media channels the importance of disseminating messages through them is of high importance for governments, organizations, companies etc. The online social network services are several and changes rapidly as they grow and evolve. Being networks, the services give the user the tools to send, as well as receive text and information. This proposes us with yet another obstacle in communication via online social network services since sender and receiver merges together. Online social network services and the Blogosphere, which essentially also is a network, exist in the context of Web 2.0. The crucial feature of Web 2.0 is to a large degree the harnessing of collective intelligence i.e. the collection of individual knowledge and information. Many of the tools and sites within Web 2.0 are therefore of a network structure, hence further stressing the importance to communicate via networks in general. Network Analysis is the discipline through which we can see and understand the larger patterns of networks. In this thesis I have looked into three key concepts of Network Analysis; Weak Links, Growth and Preferential Attachment. I have found that we can use the knowledge of Network Analysis to disseminate messages via online social network services since it provides us with the raw structures of how networks tend to grow, and how messages tend to disseminate. Title: Web 2.0 and Network Society – PR and Communication: The Challenge of Online Social Networks Number of pages: 34 Author: Max Tandefelt Tutor: Else Nygren Course: Media and Communication Studies C Period: HT 07 University: Division of Media and Communication, Department of Information Science, Uppsala University. Purpose/Aim: Facilitate message dissemination through online social network services, as they are becoming one of the dominant media channels Material/Method: Network Analysis Main results: I have presented crucial concepts of Network Analysis that can be used for message dissemination via online social network services Keywords: Online Social Network Services, Network Analysis, Web 2.0, Message Dissemination

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Recommender systems are widely used to achieve a constantly growing variety of services. Alongside with social networks, recommender systems that take into account friendship or trust between users have emerged. In this thesis, we propose an evolution of trust-based recommender systems adapted to decentralized architectures that can be deployed on top of existing social networks. Users profiles are stored locally and are exchanged with a limited, user-defined, list of trusted users. Our approach takes into account friends' similarity and propagates recommendation to direct friends in the social network in order to prevent ratings from being globally known. Moreover, the computational complexity is reduced since calculations are performed on a limited dataset, restricted to the user's neighborhood. On top of this propagation, our approach investigates several aspects. Our system computes and returns to the final user a confidence on the recommendation. It allows the user to tune his/her choice from the recommended products. Confidence takes into account friends' recommendations variance, their number, similarity and freshness of the recommendations. We also propose several heuristics that take into account peer-to-peer constraints, especially regarding network flooding. We show that those heuristics decrease network resources consumption without sacrificing accuracy and coverage. We propose default scoring strategies that are compatible with our constraints. We have implemented and compared our approach with existing ones, using multiple datasets, such as Epinions and Flixster. We show that local information with default scoring strategies are sufficient to cover more users than classical collaborative filtering and trust-based recommender systems. Regarding accuracy, our approach performs better than others, especially for cold start users, even if using less information.

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.

Service composition has risen from the need to make information systems more flexible and open. The Service Oriented Architecture has become the reference architecture model for applications carried by the impetus of Internet (Web). In fact, information systems are able to expose interfaces through the Web which has increased the number of available Web services. On the other hand, with the emergence of the Web 2.0, service composition has evolved toward web users with limited technical skills. Those end-users, named Y generation, are participating, creating, sharing and commenting content through the Web. This evolution in service composition is translated by the reference paradigm of Mashup and Mashup editors such as Yahoo Pipes! This paradigm has established the service composition within end users community enabling them to meet their own needs, for instance by creating applications that do not exist. Additionally, Web 2.0 has brought also its social dimension, allowing users to interact, either directly through the online social networks or indirectly by sharing, modifying content, or adding metadata. In this context, this thesis aims to support the evolving concept of service composition through meaningful contributions. The main contribution of this thesis is indeed the introduction of the social dimension within the process of building a composite service through end users' dedicated environments. In fact, this concept of social dimension considers the activity of compositing services (creating a Mashup) as a social activity. This activity reveals social links between users based on their similarity in selecting and combining services. These links could be an interesting dissemination means of expertise, accumulated by users when compositing services. In other terms, based on frequent composition patterns, and similarity between users, when a user is editing a Mashup, dynamic recommendations are proposed. These recommendations aim to complete the initial part of Mashup already introduced by the user. This concept has been explored through (i) a step-by-step Mashup completion by recommending a single service at each step, and (ii) a full Mashup completion approaches by recommending the whole sequence of services that could complete the Mashup. Beyond pushing a vision for integrating the social dimension in the service composition process, this thesis has addressed a particular constraint for this recommendation system which conditions the interactive systems requirements in terms of response time. In this regard, we have developed robust algorithms adapted to the specificities of our problem. Whereas a composite service is considered as a sequence of basic service, finding similarities between users comes first to find frequent patterns (subsequences) and then represent them in an advantageous data structure for the recommendation algorithm. The proposed algorithm FESMA, provide exactly those requirements based on the FSTREE structure with interesting results compared to the prior art. Finally, to implement the proposed algorithms and methods, we have developed a Mashup creation framework, called Social Composer (SoCo). This framework, dedicated to end users, firstly implements abstraction and usability requirements through a workflow-based graphic environment. As well, it implements all the mechanisms needed to deploy composed service starting from an abstract description entered by the user. More importantly, SoCo has been augmented by including the dynamic recommendation functionality, demonstrating by the way the feasibility of this concept.