Objectives: The aim of this study was to gain a thorough understanding of parents’ perceptions of and experiences with the care networks surrounding young children (<=12 years) with inherited metabolic diseases (IMDs). Methods: In this mixed methods study, parent participants created a ‘care map’ depicting their child’s network of care providers. We analyzed care maps using social network analysis. A subset of parents participated in a semi-structured interview. We analyzed interviews thematically and integrated quantitative and qualitative results narratively. Results: Sixty parents contributed care maps and 10 participated in interviews. Parent-drawn care networks were large with few connections between providers. Parents felt responsible for creating and maintaining care networks and for coordinating care. They valued providers who trusted them as part of their child’s health care team. Conclusions: Our findings highlight the complexity of care for children with IMDs and can inform the design of interventions to improve care.

Genom en systematisk litteraturöversikt i kombination med en intervju är syftet med denna uppsats att försöka beskriva varför den sociala nätverksanalysen är lämplig i brottsutredningssammanhang samt hur den sociala nätverksanalysen används i brottsutredningar. Tanken är också att översikten ska kunna bidra till att se vilka möjligheter det finns att praktiskt utveckla metoden. Det finns en hel del forskning kring både social nätverksanalys (SNA) som metod och som teori och det används idag inom en mängd olika områden. Som teori handlar SNA om hur vi människor är sociala varelser som påverkar varandra i de tankar vi har och i de val som vi gör. Som metod är SNA istället olika matematiska uträkningar som kan användas för att beskriva mänskliga relationer. Inom kriminologin är SNA relativt nytt trots att brott i sig ofta är ett ”nätverksfenomen”. Flera kriminologiska teorier trycker också på betydelsen av att den egna brottsligheten har ett samband med de personer som vi umgås med. Resultatet visar att det finns klara fördelar med att använda sig av SNA i en brottsutredning, strukturer och nyckelpersoner kan identifieras, något som inte alltid hade kunnat ske utan teknikens hjälp. Den data som i utredningssammanhang används till nätverksanalyser är vanligtvis kvantitativa data, exempelvis telefontrafik. Olika typer av data kan ge väldigt olika resultat och blir det fel i datainsamling kan det sabotera för hela analysen. Det behövs mer teoretisk forskning kring SNA för att den som metod ska kunna appliceras på kriminologisk teori och på sikt även kunna användas bättre i utredningssammanhang. Ett stort problem med att forska om metoden är att den kvantitativa datan kan vara svår att få tag på, det finns därför väldigt lite litteratur om hur social nätverksanalys kan användas i brottsutredningar. / With a systematic literature review and an interview, the aim of this essay is to try to describe how the social network analysis (SNA) is used in criminal investigations. Hopefully, the essay can also help in pointing out why future research is needed and in what direction that research should go. As a theory, SNA focuses on man as a social being and how we affect each other in the way we think and act. As a method SNA is a number of mathematical computations that aims to explain relationships. There is a large amount of research about social network analysis, both as a theory and as a method but in the criminological field SNA is still relatively new. That is surprising as many criminological theories focuses on the importance of the people we engage with and our own delinquency. The result in this essay shows that there are many advantages with using SNA in a criminal investigation, structures and key-persons becomes more visible which sometimes is hard without technology. Different types of data can generate very different results and if something goes wrong in the collection of data it can sabotage the entire analysis. There is a need for more theoretical research on SNA so that it, as a method, can be applied to criminological theory and later to criminal investigations. There is a big problem when doing research about social networks, the access to network-data. It is very hard to collect and is usually only available to police-officers or other qualified groups. Therefore the amount of literature in the subject is limited.

Currently the network centric operation and network centric warfare have generated a new area of research focused on determining how hierarchical organizations composed by human beings and machines make decisions over collaborative environments. One of the most stressful scenarios for these kinds of organizations is the so-called extreme events. This dissertation provides a hybrid simulation methodology based on classical simulation paradigms combined with social network analysis for evaluating and improving the organizational structures and procedures, mainly the incident command systems and plans for facing those extreme events. According to this, we provide a methodology for generating hypotheses and afterwards testing organizational procedures either in real training systems or simulation models with validated data. As long as the organization changes their dyadic relationships dynamically over time, we propose to capture the longitudinal digraph in time and analyze it by means of its adjacency matrix. Thus, by using an object oriented approach, three domains are proposed for better understanding the performance and the surrounding environment of an emergency management organization. System dynamics is used for modeling the critical infrastructure linked to the warning alerts of a given organization at federal, state and local levels. Discrete simulations based on the defined concept of "community of state" enables us to control the complete model. Discrete event simulation allows us to create entities that represent the data and resource flows within the organization. We propose that cognitive models might well be suited in our methodology. For instance, we show how the team performance decays in time, according to the Yerkes-Dodson curve, affecting the measures of performance of the whole organizational system. Accordingly we suggest that the hybrid model could be applied to other types of organizations, such as military peacekeeping operations and joint task forces. Along with providing insight about organizations, the methodology supports the analysis of the "after action review" (AAR), based on collection of data obtained from the command and control systems or the so-called training scenarios. Furthermore, a rich set of mathematical measures arises from the hybrid models such as triad census, dyad census, eigenvalues, utilization, feedback loops, etc., which provides a strong foundation for studying an emergency management organization. Future research will be necessary for analyzing real data and validating the proposed methodology.

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.

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.

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.

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.

This research builds on the social movements theory of networks and coalition building, the theory of digital activism, and the social networks theory of organizations to study the rich case of online mobilization for the 2011 Egyptian revolution. I use the analytical tools of social network analysis to study Twitter networks of activists of the Egyptian revolution in early 2011, when solidarity characterized the movement, and late 2014, when schism spread it apart. In this, I investigate how the repertoire of online activism relates to the on-the-ground movement. The social movements theory of networks states that activists’ ideological congruence, the presence of bridge builders who bring the movement together, and the presence of previous ties among the activists are all factors of coalition building and movement solidarity. This dissertation tested the role of these factors in the Twitter networks of Egyptian activists. The results suggest that digital activism complements rather than mirrors on-the-ground activism. While all three factors influence the network, they manifest somewhat differently than research has suggested they do in offline networks. This dissertation contributes to social movements theory of coalition building through adding validity to its application to digital activism, and suggests modifications to be made while applying this theory to the repertoire of online mobilization. The research has a methodological contribution through using cutting edge techniques of social network analysis to study Twitter networks of activists. Unlike earlier studies on the Egyptian revolution, this methodological approach revealed new findings that could not have been studied through other methods of research. / Dissertation / Doctor of Philosophy (PhD)