No description available.

Thesis (Ph. D.)--Case Western Reserve University, 2010. / Title from PDF (viewed 2010-01-28). Department of English. Includes abstract. Includes bibliographical references and appendices. Available online via the OhioLINK ETD Center.

Thesis (Ph. D.)--UCLA, 2009. / Vita. Description based on print version record. Includes bibliographical references (leaves 134-141).

Thesis (M.A.) -- Central Connecticut State University, 2009. / Thesis advisor: Rebecca Wood. "... in partial fulfillment of the requirements for the degree of Master of Arts in General Psychology." Includes bibliographical references (leaves 34-40). Abstract available via the World Wide Web.

Thesis (M. Psych.Org.)--University of Queensland, 2002. / Includes bibliographical references.

The rapid development of online social networks (OSNs) renders them a powerful platform for information diffusion on a massive scale. OSNs generate enormous propagation traces. An important question is how to model the real-world information diffusion process. Although considerable studies have been conducted in this field, the temporal characteristics have not been fully addressed yet. This thesis addresses the issue of modeling the temporal dynamics of the information diffusion process. Based on empirical findings drawn from large-scale propagation traces of a popular OSN in China, we demonstrate that the temporal characteristics has a significant impact on the diffusion dynamics. Hence, a series of new temporal information diffusion models have been proposed by incorporating these temporal features. Experimental results demonstrate that these proposed models are more accurate and practical than existing discrete diffusion models. Moreover, one application of information diffusion models, i.e., the revenue maximization problem, is studied. Specifically, the thesis consists of three major parts: 1) preliminaries, i.e., introduction of research platform and collected dataset, 2) modeling social influence diffusion from three different temporal aspects, and 3) monetizing OSNs through designing intelligent pricing strategies in the diffusion process to realize the goal of revenue maximization. Firstly, the research platform is introduced and the statistical properties of the data derived from this platform are investigated. We choose Renren, the dominant social network website in China, as our research platform and study its information propagation mechanisms. Specifically, we concentrate on the propagation of “sharing video” behaviors, and collect data on more than 2.8 million Renren users and over 209 million diffusion traces. The analysis result shows that the video access patterns in OSNs differ significantly from Youtube-like systems, which makes understanding the video propagation behaviors in OSNs an important research task. Secondly, the temporal modeling of information diffusion is explored. By investigating temporal features using real diffusion traces, we find that three factors should be considered in building realistic diffusion models, including, information propagation latency, multiple influential sources and user diversities. We then develop models to explain the information propagation process by incorporating these factors, and demonstrate that the models reflect reality well. Finally, revenue maximization in the information diffusion process is studied. Specifically, the pricing factor is explicitly incorporated into the product diffusion process. To realize the goal of revenue maximization, we develop a Dynamic Programming Based Heuristic (DPBH) to obtain the optimal pricing sequence. Application of the DPBH in the revenue maximization problem shows that it performs well in both the expected revenue achieved and in running time. This leads to fundamental ramifications to many related OSN marketing applications. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Information diffusion in online social networks has received attention in both research and actual applications. The prevalence of online social networking sites offers the possibility of mining for necessary information. However, existing influence maximization algorithms and newly proposed influence diffusion models do not distinguish between seed nodes (or pilot users) and nonseed nodes and assume all nodes are cooperative in propagating influence. This thesis investigates models and heuristics for noncooperative information diffusion in online social networks. It consists of three parts: tragedy of the commons in online social search (OSS), influence maximization in noncooperative social networks under the linear threshold model (LTM), and influence maximization in noncooperative social networks under the independent cascade model (ICM). Firstly, the tragedy of the commons problem in OSS is considered. I propose an analytical model that captures the behavior of OSS nodes, and, from a gaming-strategy point of view, analyze various strategies an individual node can utilize to allocate its awareness capacity. Based on this I derive the Pareto inefficiency in terms of the system cost. An incentive scheme which can lead selfish nodes to the “social optimal” state of the whole system is also proposed. Extensive simulations show that the strategy with our proposed incentive mechanism outperforms other strategies in terms of the system cost and the search success rate. The second part of the thesis presents the first detailed analysis of influence maximization in noncooperative social networks under the LTM. The influence propagation process is structured into two stages, namely, seed node selection and influence diffusion. In the former, I introduce a generalized maximum-flow-based analytical framework to model the noncooperative behavior of individual users and develop a new seed node selection strategy. In the latter, I propose a game-theoretic model to characterize the behavior of noncooperative nodes and design a Vickrey-Clarke-Groves-like (VCG-like) scheme to incentivise cooperation. Then I study the budget allocation problem between the two stages, and show that a marketer can utilize the two proposed strategies to tackle noncooperation intelligently. The proposed schemes are evaluated on large coauthorship networks, and the results show that the proposed seed node selection scheme is very robust to noncooperation and the VCG-like scheme can effectively stimulate a node to become cooperative. Finally, I study the influence maximization problem in noncooperative social networks under the ICM using the same two-stage framework originally proposed for LTM. For the seed selection stage, a modified hierarchy-based seed node selection strategy which can take node noncooperation into consideration is introduced. The VCG-like incentive scheme designed for the influence diffusion stage under LTM can also be utilized for ICM in a similar manner. Then I also study the budget allocation problem between the two stages. The evaluation results show that the performance of the hierarchy-based seed node selection scheme is satisfactory in a noncooperative social network and the VCG-like scheme can effectively encourage node cooperation. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Advances in smart technologies, wireless networking, and increased interest in contextual services have led to the emergence of ubiquitous and pervasive computing as one of the most promising areas of computing in recent years. Smart Spaces, in particular, have gained significant interest within the research community. Currently, most Smart Spaces rely on physical components, such as sensors, to acquire information about the real-world environment. Although current sensor networks can acquire some useful contextual information from the physical environment, their information resources are often limited, and the data acquired is often unreliable. We argue that by introducing social network information into such systems, smarter and more adaptive spaces can be created. Social networks have recently become extremely popular, and are now an integral part of millions of people’s daily lives. Through social networks, users create profiles, build relationships, and join groups, forming intermingled sets and communities. Social Networks contain a wealth of information, which, if exploited properly, can lead to a whole new level of smart contextual services. A mechanism is therefore needed to extract data from heterogeneous social networks, to link profiles across different networks, and to aggregate the data obtained. We therefore propose the design and implementation of a Smart Spaces framework that utilizes the social context. In order to manage services and sessions, we integrate our system with the IP Multimedia Subsystem. Our system, which we call SocioSpace, includes full design and implementation of all components, including the central server, the location management system, the social network interfacing system, the service delivery platform, and user agents. We have built a prototype for proof of concept and carried out exhaustive performance analysis; the results show that SocioSpace is scalable, extensible, and fault-tolerant. It is capable of creating Smart Spaces that can truly deliver adaptive services that enhance the users’ overall experience, increase their satisfaction, and make the surroundings more beneficial and interesting to them. / Thesis (Ph.D, Computing) -- Queen's University, 2012-04-30 11:14:29.054

<p>This thesis aims towards contributing to the understanding of the role of social networks in the context of developing countries. It contains two chapters that take on different aspects of social networks. </p><p>In the first chapter, I study a specific characteristic of social networks, network structure, which is the way in which households are connected in a social network. This chapter looks at how social networks help facilitate cooperation among people in the community, at least, in the context of contributing to public goods. Public goods in this case refer to shared infrastructure in rural villages such as community buildings, water wells, and roads. Provision and maintenance of these public goods relies on contribution of people within the vilage and therefore is essentially a problem of voluntary collective action. In particular, I study whether the level of network connectedness of a household (as measured by network centrality) affects its decisions to contribute to public goods, using data from the Gambia. As an identification strategy, I use an instrumental variable approach that exploits the arguably exogenous variation in village ethnic composition, largely determined by historical accident. The findings suggest that better-connected households contribute more to some public goods. The network position effect is smaller when using a centrality measure that accounts for indirectly connected households. This paper also offers networks as a potential mechanism that explains the long-established relationship between ethnic diversity and public goods.</p><p>The second chapter (co-authored with Alessandro Tarozzi and Aprajit Mahajan) looks on a different aspect of social networks. In this chapter, we describe evidence of limited diffusion of bednet acquisition and usage from beneficiaries of an ITN distribution program in rural Orissa, India, to households that did not receive bednets during the intervention. Identification of such network effects relies on the change in ITN adoption among the beneficiaries of a program of bednet distribution that was carried out in a randomly selected subset of 141 study villages. This field experiment was designed to increase the adoption rate of insecticide-treated bed-nets to protect against malaria. The program randomly assigned 141 sample villages into 3 experimental arms - a group in which some households received free distribution of bed-nets, a group in which micro loans for bed-nets were made available, and finally a control group with no intervention. In this paper, we focus on the impact of the intervention on households who lived in these respective groups of villages but did not receive the intervention. Our sample households include those that were exposed to the program via interactions with treated households. Identification is possible by exploiting the exogenous variation from the randomized controlled trials. We find that there is a small positive association between the number of social connections with treated households and their bed nets usage. On average, spillovers were limited. However, we find that bednet usage (but not acquisition) was substantively and significantly associated with some (but not all) measures of social links between non-beneficiaries and beneficiaries. This provides evidence, although limited, of network effects in the adoption of a health-related technology possibly due to diffusion of information and peer imitation.</p> / Dissertation

The perceived availability of social support has been documented as a protective mechanism among adults and adolescents but little work has been done with children with emotional and behavioral difficulties (EBD). The current research addressed this gap in literature with a sample of teacher-nominated children with EBD. Fifty-four children (43 males, 11 females), 56 parents, and 27 teachers completed questionnaires on perceived social support and behavioral, emotional, and social functioning. Results suggest a pattern of domain specific social support relating to domain specific adjustment outcomes. Furthermore, the importance of child ratings over parent and teacher ratings of social support was evident. Significant inter-rater differences were found on ratings of social support, despite directional agreement. Finally, children with higher ratings of social support reported more positive adjustment and less negative adjustment than children with lower support ratings. Interventions that are child-centered and domain specific are suggested.