This dissertation addresses the problem of influence maximization in social networks. In- fluence maximization is applicable to many types of real-world problems, including modeling contagion, technology adoption, and viral marketing. Here we examine an advertisement domain in which the overarching goal is to find the influential nodes in a social network, based on the network structure and the interactions, as targets of advertisement. The assumption is that advertisement budget limits prevent us from sending the advertisement to everybody in the network. Therefore, a wise selection of the people can be beneficial in increasing the product adoption. To model these social systems, agent-based modeling, a powerful tool for the study of phenomena that are difficult to observe within the confines of the laboratory, is used. To analyze marketing scenarios, this dissertation proposes a new method for propagating information through a social system and demonstrates how it can be used to develop a product advertisement strategy in a simulated market. We consider the desire of agents toward purchasing an item as a random variable and solve the influence maximization problem in steady state using an optimization method to assign the advertisement of available products to appropriate messenger agents. Our market simulation 1) accounts for the effects of group membership on agent attitudes 2) has a network structure that is similar to realistic human systems 3) models inter-product preference correlations that can be learned from market data. The results on synthetic data show that this method is significantly better than network analysis methods based on centrality measures. The optimized influence maximization (OIM) described above, has some limitations. For instance, it relies on a global estimation of the interaction among agents in the network, rendering it incapable of handling large networks. Although OIM is capable of finding the influential nodes in the social network in an optimized way and targeting them for advertising, in large networks, performing the matrix operations required to find the optimized solution is intractable. To overcome this limitation, we then propose a hierarchical influence maximization (HIM) iii algorithm for scaling influence maximization to larger networks. In the hierarchical method the network is partitioned into multiple smaller networks that can be solved exactly with optimization techniques, assuming a generalized IC model, to identify a candidate set of seed nodes. The candidate nodes are used to create a distance-preserving abstract version of the network that maintains an aggregate influence model between partitions. The budget limitation for the advertising dictates the algorithm’s stopping point. On synthetic datasets, we show that our method comes close to the optimal node selection, at substantially lower runtime costs. We present results from applying the HIM algorithm to real-world datasets collected from social media sites with large numbers of users (Epinions, SlashDot, and WikiVote) and compare it with two benchmarks, PMIA and DegreeDiscount, to examine the scalability and performance. Our experimental results reveal that HIM scales to larger networks but is outperformed by degreebased algorithms in highly-connected networks. However, HIM performs well in modular networks where the communities are clearly separable with small number of cross-community edges. This finding suggests that for practical applications it is useful to account for network properties when selecting an influence maximization method.

Insight into opportunities for process improvement provides a competitive advantage through increases in organizational effectiveness and innovation As a result, it is important to understand the conditions under which employees are willing to communicate this information. This study examined the relationship between trust and psychological safety on the willingness to report errors in a medical setting. Trust and psychological safety were measured at the team and leader level. In addition, the moderating effect of a learning orientation climate at three levels of the organization (i.e., team members, team leaders, organizational) was examined on the relationship between trust and psychological safety on willingness to report errors. Traditional surveys and social network analysis were employed to test the research hypotheses. Findings indicate that team trust, when examined using traditional surveys, is not significantly associated with informally reporting errors. However, when the social networks within the team were examined, evidence that team trust is associated with informally discussing errors was found. Results also indicate that trust in leadership is associated with informally discussing errors, especially severe errors. These findings were supported and expanded to include a willingness to report all severity of errors when social network data was explored. Psychological safety, whether within the team or fostered by leadership, was not found to be associated with a willingness to informally report errors. Finally, learning orientation was not found to be a moderating variable between trust and psychological safety on a willingness to report errors. Instead, organizational learning orientation was found to have a main effect on formally reporting errors to risk management and documenting errors in patient charts. Theoretical and practical implications of the study are offered.

Substance use, such as alcohol, cigarette, and marijuana, is a threat to the health and well-being of the youth, their families, and society as well. Government supports and implements several programs to protect youth from substance use. The aim of this study is to evaluate the impact of social capital on youth behavior and to suggest evidence-based policy interventions. Social capital refers to individual embeddedness in web of social relations and their behaviors guided by social structure. Therefore, adolescents' social interactions with their peers, parents, and community were investigated. The substance use was measured by the usage of cigarettes, alcohol, marijuana, and inhalants in the past year. The type of activities adolescents participate in, the time and type of intra-familial interactions between parents and adolescents, and the type of peer groups adolescents interact with were employed as indicators of social capital. In other words, this study focuses on the relationship between youth substance use and the impact of parents, peers, and youth activities. Moreover, the study examined not only the correlation between social capital and substance use, but also the variation in substance use among youth by age, gender, ethnicity, income level, and mobility. The data, National Survey on Drug Use and Health (2005, 2006, and 2007), was collected by the United States Department of Health and Human Service, Substance Abuse and Mental Health Services Administration Office of Applied Studies. The sample size for each year was around 17.000. Structural Equation Modeling (SEM) was used to test the hypothesized. The results of the statistical analysis supported the research hypothesis. Findings show that there is a relationship between youth substance use and social capital. All three dimensions of social capital (peer impact, family attachments, and youth activities) were found to be statistically significant. While peer influence is positively correlated with substance use, family attachment and youth activities have a negative relationship with substance use. The impact of social capital however varies by age, gender, ethnicity, mobility, and income level. The study also contributes to the social capital literature by integrating different perspectives in social capital and substance use literature. Moreover, it successfully demonstrates how social capital can be utilized as a policy and intervention tool.

The aim of this PhD thesis is to investigate the political role of Adelheid of Burgundy in tenth-century Europe. Adelheid was certainly one of the central figures of the Ottonian dynasty during her years as empress and during her widowhood. The systematic study of the diplomas in which she acted as mediator alongside Otto I, Otto II and Otto III was an attempt to understand the basis of her political relevance. The result of the diplomatic research was analyzed through the method of social network analysis, which offered a new and global point of view on the issue and allowed to better focus on the various actors that composed the network of relationships of Adelheid during her life. / Lo scopo della presente tesi di dottorato è l’analisi del ruolo politico di Adelaide di Borgogna nell’Europa del secolo X. Adelaide fu certamente una figura di spicco all’interno della dinastia ottoniana sia in qualità di imperatrice al fianco di Ottone I sia negli anni della vedovanza. Lo studio sistematico dei diplomi in cui la sovrana venne indicata come mediatrice presso il marito, il figlio e il nipote ha rappresentato il punto di partenza per indagare le basi e le motivazioni della sua rilevanza politica. In particolare, il risultato della ricerca diplomatica è stato esaminato attraverso la metodologia della social network analysis che ha offerto un punto di vista nuovo e globale sulla questione e ha permesso di individuare più chiaramente i vari attori che composero la rete di relazioni dell’imperatrice nell’intero corso della sua vita.

The abundance of data in the information age poses an immense challenge for us: how to perform large-scale inference to understand and utilize this overwhelming amount of information. Such techniques are of tremendous intellectual significance and practical impact. As part of this grand challenge, the goal of my Ph.D. thesis is to develop effective and efficient statistical topic models for massive text collections by incorporating extra information from other modalities in addition to the text itself. Text documents are not just text, and different kinds of additional information are naturally interleaved with text. Most previous work, however, pays attention to only one modality at a time, and ignore the others. In my thesis, I will present a series of probabilistic topic models to show how we can bridge multiple modalities of information, in a united fashion, for various tasks. Interestingly, joint inference over multiple modalities leads to many findings that can not be discovered from just one modality alone, as briefly illustrated below: Email is pervasive nowadays. Much previous work in natural language processing modeled text using latent topics ignoring the social networks. On the other hand, social network research mainly dealt with the existence of links between entities without taking into consideration the language content or topics on those links. The author-recipient-topic (ART) model, by contrast, steers the discovery of topics according to the relationships between people, and learns topic distributions based on the direction-sensitive messages sent between entities. However, the ART model does not explicitly identify groups formed by entities in the network. Previous work in social network analysis ignores the fact that different groupings arise for different topics. The group-topic (GT) model, a probabilistic generative model of entity relationships and textual attributes, simultaneously discovers groups among the entities and topics among the corresponding text. Many of the large datasets do not have static latent structures; they are instead dynamic. The topics over time (TOT) model explicitly models time as an observed continuous variable. This allows TOT to see long-range dependencies in time and also helps avoid a Markov model's risk of inappropriately dividing a topic in two when there is a brief gap in its appearance. By treating time as a continuous variable, we also avoid the difficulties of discretization. Most topic models, including all of the above, rely on the bag of words assumption. However, word order and phrases are often critical to capturing the meaning of text. The topical n -grams (TNG) model discovers topics as well as meaningful, topical phrases simultaneously. In summary, we believe that these models are clear evidence that we can better understand and utilize massive text collections when additional modalities are considered and modeled jointly with text.

My dissertation is an ethnographic study of Asian-American users on the social network site, Xanga. Based on my analysis of online texts, responses to texts, and participants' discussions of their writing motivations, my research strongly suggests that examining digital writing through participants' complex and overlapping constructions of their community and public(s) can help the field reconsider digital writing as a site of Asian-American rhetoric and as a process of constructing and transforming racial identities and relations. In particular, I examine how community and public, as interconnected and shifting writing imaginaries on Xanga, afford Asian-American users on this site the opportunity to write, explore, and circulate their racial and ethnic identities for multiple purposes and various audiences. Race and ethnicity, as many scholars argue, are shifting and unstable concepts and experiences. Therefore, writing about race and ethnicity may be done best in environments that can accommodate complex and multiple acts of racial and ethnic formations. While my research demonstrates how participants "want to be heard" on their own terms, whom they imagine (or want to imagine) as listening/reading significantly informs their writing. That is, participants' conceptions of their writing goals and their audiences are multiple and simultaneous--these racial and ethnic writing acts are often inflected by intersecting issues of gender, sexuality, class, culture, and intergenerational tensions--and, hence, traditional writing genres that limit such goals, audiences, and complexity do not always reflect how writers conceive of their own racial and ethnic experiences and their writing in the world. This study, then, examines Xanga as a flexible writingecologythat affords Asian-American users opportunities to compose their continuously transforming and complex racial and ethnic identities across multiple niches of representational sites and, specifically, in public and community spaces.

U.S. politics, media, and citizens are highly polarized, stipulating that society is divided between Democrats and Republicans (Hameleers, 2019). The U.S. has seen an increased political polarization over the past 25 years (Heltzel & Laurin, 2020; Westfall, Van Boven, Chambers, & Judd, 2015). Technological development in the campaign environment has fueled this political polarization (Hong & Kim, 2016). In such a polarized technological society, partisan news media cover political issues and events from their ideological perspective (Arceneaux, Johnson, & Murphy, 2012), which may affect the polarized citizens.The Republican Party is conservative, while the Democratic Party is liberal (Westfall, Van Boven, Chambers, & Judd, 2015). Each party has issue agendas that they prioritize during the campaign. When political campaigns post a message on social media, they not only post just one issue but several related issues. These interlinked issues have a networked effect on the partisan news media and the polarized citizens (McCombs, Shaw, & Weaver, 2014). How political campaigns interlinked different issue agendas during campaigns in a polarized environment has not been investigated. This study intends to see the similarities and dissimilarities between the Democratic and Republican Party issue networks using a network agenda setting theory during the 2020 U.S. presidential campaign and how they build and set networked issue agendas in the partisan news media and the polarized public on Facebook. The study uses a hybrid content analysis and network analysis of issue agendas presented by the Biden and Trump campaigns, partisan media (CNN and Fox News), and the Democratic Party and the Republican Party supporters on Facebook. Facebook posts are collected using Facebook’s CrowdTangle Search option from January 1, 2021, to November 3, 2020. This study uses a hybrid content analysis method which engages with both human coders and computational means to analyze big data sets (Guo et al., 2016). The data analysis involves measuring core-periphery block model, clique analysis, network visualization, and Quadratic Assignment Procedures (QAP). A social networking analysis software, UCINET, is used for measuring core-periphery block model, clique analysis, and QAP correlations(Borgatti, Everett, & Johnson, 2018). The scholarship of political campaign communication needs to reconnect to the ideological positions of political campaigns, partisan news media, and party supporters. This holistic study is significant in terms of better understanding the mechanism of networked agenda-setting activities of presidential campaigns in a polarized environment on Facebook. Methodologically, this study offers new techniques for investigating networked issue agendas of campaigns, news media, and citizens. It uses core-periphery block model and clique analysis as indicators of network agenda building and network agenda-setting influences. Social media practitioners like campaign managers can consider the political polarization, fragmented nature of social media, and polarized audience during political campaigning.

This  thesis  contributes  to  the  advancement  on  the  network  view  of  social  capital andentrepreneurship, focusing on science-based innovation by observatory social network research. The study has conducted a survey to construct a network map of and network attitudes in Zulu science park (alias name) located in Sweden. The analysis showed that there were relatively positive expectations to utilize network more effectively and  efficiently in their business activity with respect to sharing of research skills and resources, as well as social exchange in particular. Currently, the science park seems not yet to take advantage of the potential momentum of the respondents or bottom-up initiatives where tenants maintain the environment mutually under trust. I discuss the merits and challenges in such resource and knowledge sharing in the business development support and governance as a new way of unique business incubator and science-park management, focusing on networking.

With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems. / Commonwealth Scholarship Commission.

Introduction: In a mixed-methods study I assessed the role of social networks as predictors and outcomes of the implementation of an intervention to promote evidence-informed decision-making (EIDM) in three public health departments in Ontario, Canada. The quantitative strand included the analysis of the role of staff’s position in networks on the adoption of EIDM, the longitudinal evolution of networks, and the association between the name generators’ position in surveys and respondents’ motivation to answer survey questions. The qualitative strand aimed to explain and contextualize the quantitative findings. Methods: A tailored intervention was implemented in the public health departments, including the mentoring of staff through the EIDM process by a knowledge broker. The staff participated in three online surveys before and after the 22-month intervention, providing the names of peers to whom they turned to seek information, whom they considered as experts, and their friends. I assessed the dynamic evolution of social networks, and the role of local opinion leaders (OL) in promoting the adoption of EIDM. I interviewed key network actors about their interpretation and experience regarding the quantitative findings. Results: Overall, there was no statistically significant impact on EIDM behavior and skill in health departments. However, the analysis of the role of OLs in behaviour change showed that non-engaged staff who were connected to highly engaged OLs, and those OLs who communicated with each other improved their EIDM behavior. Social networks became more centralized around already popular staff due to selective training of recognized experts. Highly engaged staff tended to connect to each other, and to limit their connections within organizational divisions over time. In the department where multiple activities were being implemented to support EIDM, the highly engaged staff became more popular due to department-wise presentations and informal information spread. I also found that when name generator questions are asked later in surveys then respondents are more likely to refuse, indicate they do not know anyone, or provide fewer names than when these questions are asked earlier Conclusion: Social network analysis showed the structure of information-seeking relations, the impact of opinion leaders on the EIDM behavior of their peers, and underlying social changes through implementing an EIDM intervention. These findings can inform the design and tailoring of EIDM interventions in public health organizations. / Thesis / Doctor of Philosophy (PhD) / In three public health departments in Ontario, where we offered an intervention to a group of staff on how to use more research evidence in practice, I studied how the pattern of communication among staff influenced their use of evidence, how those communications changed over time, and how the staff themselves viewed those changes. In the department that largely promoted staff engagement in the intervention, the staff who were engaged became more popular over time. In all departments, already popular staff became more popular. The staff who sought information from popular people engaged in the intervention, and those popular people who communicated with each other used more research evidence over time. Network analysis helped reveal the social structure and identify popular staff and could be used to inform similar interventions. It also showed how selecting and training a group of staff can change the way people communicate in health departments.