The penetration of social media today enables the rapid spread of breaking news and other developments to millions of people across the globe within hours. However, such pervasive use of social media by the general masses to receive and consume news is not without its attendant negative consequences as it also opens opportunities for nefarious elements to spread rumors or misinformation. A rumor generally refers to an interesting piece of information that is widely disseminated through a social network and whose credibility cannot be easily substantiated. A rumor can later turn out to be true or false or remain unverified. The spread of misinformation and fake news can lead to deleterious effects on users and society. The objective of the proposed research is to develop a range of machine learning methods that will effectively detect and characterize rumor veracity in social media. Since users are the primary protagonists on social media, analyzing the characteristics of information spread w.r.t. users can be effective for our purpose. For our first problem, we propose a method of computing user embeddings from underlying social networks. For our second problem, we propose a long short-term memory (LSTM) based model that can classify whether a story discussed in a thread can be categorized as a false, true, or unverified rumor. We demonstrate the utility of user features computed from the first problem to address the second problem. For our third problem, we propose a method that uses user profile information to detect rumor veracity. This method has the advantage of not requiring the underlying social network, which can be tedious to compute. For the last problem, we investigate a rumor mitigation technique that recommends fact-checking URLs to rumor debunkers, i.e., social network users who are very passionate about disseminating true news. Here, we incorporate the influence of other users on rumor debunkers in addition to their previous URL sharing history to recommend relevant fact-checking URLs. / Doctor of Philosophy / A rumor is generally defined as an interesting piece of a story that cannot be authenticated easily. On social networks, a user can generally find an interesting piece of news or story and may share (retweet) it. A story that initially appears plausible can later turn out to be false or remain unverified. The propagation of false rumors on social networks has a deteriorating effect on user experience. Therefore, rumor veracity detection is important, and drawing interest in social network research. In this thesis, we develop various machine learning models that detect rumor veracity. For this purpose, we exploit different types of information regarding users, such as profile details and connectivity with other users etc. Moreover, we propose a rumor mitigation technique that recommends fact-checking URLs to social network users who are passionate about debunking rumors. Here, we leverage similar techniques used in e-commerce sites for recommending products to solve this problem.

Yes / Introduction: The complex healthcare system that provides patients with medicines places them at risk when care is transferred between healthcare organisations, for example discharge from hospital. Consequently, under-standing and improving medicines management, particularly at care transfers, is a priority.Objectives: This study aimed to explore the medicines management system as patients experience it and determine differences in the patient-perceived importance of people in the system.Methods: We used a Social Network Analysis framework, collecting ego-net data about the importance of people patients had contact with concerning their medicines after hospital discharge. Single- and multi-level logistic regression models of patients' networks were constructed, and model residuals were explored at the patient level.This enabled us to identify patients' networks with support tie patterns different from the general patterns suggested by the model results. Qualitative data for those patients were then analysed to understand their differing experiences.Results: Networks comprised clinical and administrative healthcare staff and friends and family members.Networks were highly individual and the perceived importance of alters varied both within and between patients. Ties to spouses were significantly more likely to be rated as highly important and ties to community pharmacy staff (other than pharmacists) and to GP receptionists were less likely to be highly rated. Patients with low-value medicines management networks described having limited information about their medicines and alack of understanding or help. Patients with high-value networks described appreciating support and having confidence in staff.Conclusions: Patients experienced medicines management as individual systems within which they interacted with healthcare staff and informal support to manage their treatment. Multilevel models indicated that there are unexplained variables impacting on patients' assessments of their medicines management networks. Qualitative exploration of the model residuals can offer an understanding of networks that do not have the typical range of support ties. / National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC)

Globally, the incidence of infection caused by Neisseria gonorrhoeae is the second highest among the bacterial sexually transmitted infections. In Canada, declining rates during the 1990s suggested progress toward curbing gonorrhea; however, those have been increasing since 1999, with rates in Saskatchewan among the highest in the country. Infection can cause serious complications in men and women, and reported resistance to third-generation cephalosporins could lead to potentially untreatable infections. Increased understanding of gonorrhea transmission dynamics, sexual networks, and predictors of antimicrobial resistance development is needed to inform the development of improved approaches to prevention and treatment. The research presented herein draws upon data from Shanghai, China, and Saskatchewan, Canada, to compare and contrast varying epidemiologic approaches to enhancing understanding of gonorrhea in the two settings. Using traditional statistical approaches, multi-level statistical modeling, social network analysis, and dynamic simulation modeling, questions related to sexual behavior, partner presentation, and antimicrobial resistance development are explored. Each technique is evaluated for its potential contribution to overall understanding of the issues related to the ongoing gonorrhea epidemic, globally, and in Saskatchewan. The relative strengths and limitations of the application of the analytical approaches in the different settings are described. Socio-demographic characteristics provided useful indicators of antimicrobial resistant infection among patients with gonorrhea from Shanghai. Further, socio-demographic characteristics were also useful for predicting presentation of a partner for testing and treatment and the use of condoms during intercourse, among this study population. In Saskatchewan, socio-demographic characteristics were useful in predicting coinfection with gonorrhea and chlamydia at the time of diagnosis as well as repeat infection with gonorrhea. Social network analysis of the Saskatchewan dataset provided little additional understanding of the gonorrhea epidemic in the province. This result was largely related to how STI data are collected and stored in the province. The utility of dynamic simulation modeling to investigate the potential impact of antimicrobial resistance in Saskatchewan was also limited due to the same data constraints. However, the insight gained from the model building process and findings from the working model did offer a starting point for conversations around the best ways to postpone the development of antimicrobial resistance in N. gonorrhoeae in Saskatchewan, as well as contribute additional information about how the ways in which STI data are collected and stored in the province considerably restrict the applicability of otherwise powerful epidemiologic tools. With persistently high rates of disease transmission, and the threat of untreatable infections due to antimicrobial resistance, N. gonorrhoeae remains a substantial public health threat locally and globally. The research presented herein describes various approaches to understanding and controlling this disease, applied in contrasting settings. There are a wide variety of elements that should be considered when choosing the appropriate tool(s) to address gonorrhea in a given population; there is no “one size fits all” solution. The local epidemiology of disease, cultural and behavioural norms, the characteristics of the notifiable disease reporting and information systems, and the availability of suitable data all affect the relative strengths and weaknesses of the available analytic methods and disease control approaches.

Thesis (MSc)--Stellenbosch University, 2011 / ENGLISH ABSTRACT: With billions of current internet users interacting through social networks, the need has arisen to analyze the structure of these networks. Many authors have proposed random graph models for social networks in an attempt to understand and reproduce the dynamics that govern social network development. This thesis proposes a random graph model that generates social networks using a community-based approach, in which users’ affiliations to communities are explicitly modeled and then translated into a social network. Our approach explicitly models the tendency of communities to overlap, and also proposes a method for determining the probability of two users being connected based on their levels of commitment to the communities they both belong to. Previous community-based models do not incorporate community overlap, and assume mutual members of any community are automatically connected. We provide a method for fitting our model to real-world social networks and demonstrate the effectiveness of our approach in reproducing real-world social network characteristics by investigating its fit on two data sets of current online social networks. The results verify that our proposed model is promising: it is the first community-based model that can accurately reproduce a variety of important social network characteristics, namely average separation, clustering, degree distribution, transitivity and network densification, simultaneously. / AFRIKAANSE OPSOMMING: Met biljoene huidige internet-gebruikers wat deesdae met behulp van aanlyn sosiale netwerke kommunikeer, het die analise van hierdie netwerke in die navorsingsgemeenskap toegeneem. Navorsers het al verskeie toevalsgrafiekmodelle vir sosiale netwerke voorgestel in ’n poging om die dinamika van die ontwikkeling van dié netwerke beter te verstaan en te dupliseer. In hierdie tesis word ’n nuwe toevalsgrafiekmodel vir sosiale netwerke voorgestel wat ’n gemeenskapsgebaseerde benadering volg, deurdat gebruikers se verbintenisse aan gemeenskappe eksplisiet gemodelleer word, en dié gemeenskapsmodel dan in ’n sosiale netwerk omskep word. Ons metode modelleer uitdruklik die geneigdheid van gemeenskappe om te oorvleuel, en verskaf ’n metode waardeur die waarskynlikheid van vriendskap tussen twee gebruikers bepaal kan word, op grond van hulle toewyding aan hulle wedersydse gemeenskappe. Vorige modelle inkorporeer nie gemeenskapsoorvleueling nie, en aanvaar ook dat alle lede van dieselfde gemeenskap vriende sal wees. Ons verskaf ’n metode om ons model se parameters te pas op sosiale netwerk datastelle en vertoon die vermoë van ons model om eienskappe van sosiale netwerke te dupliseer. Die resultate van ons model lyk belowend: dit is die eerste gemeenskapsgebaseerde model wat gelyktydig ’n belangrike verskeidenheid van sosiale netwerk eienskappe, naamlik gemiddelde skeidingsafstand, samedromming, graadverdeling, transitiwiteit en netwerksverdigting, akkuraat kan weerspieël.

To αντικείμενο της παρούσας διπλωματικής εργασίας είναι η μελέτη της συμπεριφοράς των χρηστών της Wikipedia, όταν πραγματοποιούν μια τροποποίηση περιεχομένου ενός άρθρου, σε σχέση με την χώρα καταγωγής τους. Η μελέτη ξεκινάει με μια γενική παρουσίαση των ιστοσελίδων κοινωνικής δικτύωσης με έμφαση στις Wikipedia αλλά και της έρευνας του ολλανδού κοινωνιολόγου Geert Hofstede και τις θεωρίας του περί την ύπαρξη πέντε κοινωνικών διαστάσεων που μπορούν να περιγράψουν αρκετά ικανοποιητικά κάθε κράτος και τους κατοίκους του. Στην συνέχεια, κατασκευάζουμε μια εφαρμογή η οποία αντλεί και συλλέγει δεδομένα σχετικά με τις τροποποιήσεις από πέντε διαφορετικές εκδόσεις – γλώσσες της Wikipedia για 8 διαφορετικά άρθρα και τα κατηγοριοποιεί ανάλογα με το είδος της τροποποίησης. Τέλος, γίνεται η προσπάθεια εξαγωγής κάποιων συμπερασμάτων σχετικά με τον τρόπο συμπεριφοράς των χρηστών που προέρχονται από το ίδιο κράτος συγκρίνοντας τα δεδομένα που συλλέξαμε για κάθε διαφορετική γλώσσα με τις διαστάσεις που έχει μετρήσει ο Geert Hofstede για το αντίστοιχο κράτος. / The subject of this thesis is to study the behaviour of the users of Wikipedia when editing the content of an article, with respect to the country of origin of the user. The study begins with an overview of social networking websites with a focus on Wikipedia and a presentation of the research of the Dutch sociologist Geert Hofstede and his theory of the existence of five social dimensions that can describe quite well each country and its residents. Afterwards, we develop an application that draws and collects data from the article history about the edits of eight Wikipedia articles from five different editions – languages of Wikipedia and classifies them according to the type of the edit. Finally, we attempt to export some conclusions about the behaviour of users from the same country by relating the data we exported for each language to the dimensions measured by Geert Hofstede for the corresponding country.

This study examines the interconnectedness of social networks of the early adopter Family and Consumer Science Extension Agents (FCS Agents) of the Mental Healthiness and Aging Initiative (MHAI) pilot conducted in eleven (11) eastern Kentucky counties between October 2007 and April 2009 and compares the social network connections of the FCS Agents in the other six Extension Districts in Kentucky. This research used whole-network survey analysis applying the social network approach, a conceptual model for explaining the communication of new ideas and information within an organizational network. Organizational networks are important structural elements of organizational systems and key to understanding diffusion of new programs within institutional organizations, such as the Kentucky Cooperative Extension Service. Previous diffusion studies by Extension scholars have concentrated on the classic diffusion model of agricultural technology innovations with individual farmer adopters. Adoption of new programs and ideas is the process by which individuals in a social system decide to use the communicated new idea, program, and/or technology. This conceptual model describes the stages of diffusion through the attributes of the clientele adopters. The social network conceptual model describes diffusion through communication channels. Identified opinion leaders are matched with those who nominate them or closely identify with them in a diffusion network perspective to accelerate the diffusion process through an optimal pairing of network member with influencers. Data were collected from the FCS Extension Agent network in an online survey “FCS Health Information Communication Network Survey” from July 1, 2011 – July 30, 2011. Participants were asked to rate each of their co-workers in their own district, and in each of the other six districts, on how often they go to each person directly for health education information. Hypothesis testing supports the use of opinion leaders, bridges and communication structures within the social network structure of FCS agents for diffusing health programming within the Cooperative Extension Service.

Bat populations in the eastern United States are currently declining at unprecedented rates as a result of habitat loss, commercial wind energy development, and white-nose syndrome. Effective conservation of these declining populations requires knowledge of several aspects of summer and winter ecology, including daytime habitat use (day-roost selection and social behaviors), nocturnal habitat use (foraging habitat selection, prey selection, and prey abundance), and winter hibernation (torpor) patterns. This dissertation addresses these questions for Rafinesque’s big-eared bat (Corynorhinus rafinesquii), a species of conservation concern in the southeastern United States. Kentucky represents the northern edge of the range of Rafinesque’s big-eared bat, and summer and winter behaviors in Kentucky are likely to differ from what has been observed in southern portion of the range, where available habitats and climate are different. My research occurred in two study areas in Kentucky, Mammoth Cave National Park in central Kentucky, and the Ballard Wildlife Management areas in western Kentucky. This dissertation includes all of the work done in western Kentucky, where I radio-tagged 48 adult big-eared bats and documented daytime and nighttime habitat use. Also included is a portion of the work done in central Kentucky, focusing on hibernation patterns of 14 adult big-eared bats radio-tagged during the winter at Mammoth Cave. Data disseminated in this dissertation provide insights into the summer and winter ecology of Rafinesque’s big-eared bat in Kentucky, and can be used to manage populations threatened by habitat loss and white-nose syndrome.

Previous research has suggested that individuals differ in their accuracy of perceptions of the social environment, and some research suggests that powerful individuals in particular tend to be lazy, disinterested observers of the social world. A handful of field studies and lab experiments linking power with individuals’ perceptions of others’ social networks have generally supported this view. However, recent theory addressing the psychological consequences of power for the power-holder claim that in certain circumstances and for some kinds of social information, power is linked to increased accuracy of social information. This dissertation tests this idea by drawing on social network theory and the situated focus theory of power. I examine the relationship between individuals’ formal power and their perceptual accuracy of social network relationships. I propose that individuals’ perceptual accuracy is affected by 1) their formal power in the organization 2) the type of relationship being perceived (expressive/instrumental, positive/negative), and 3) the dependence relationship with the target of perception (whether the perceiver is dependent on the perceived to get their work done). Predictions were tested using cognitive social network data collected from a call center within a division of a large corporation in the US. Results showed that formal power was linked to increased accuracy for some relationship content (particularly negative expressive relationships), and managers tend to be more accurate when perceiving their own incoming relationships than non-managers.

<p>This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time.</p><p>A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation.</p><p>The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step.</p><p>In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications.</p><p>Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.</p>

One of the biggest questions surrounding common-pool natural resources (CPRs) lies in understanding the circumstances which increase the likelihood of sustainable use and those that lead to resource degradation. Small-scale fisheries are an example of a CPR that has proven difficult to manage sustainably. I use social network analysis methods to examine the social connectivity of small-scale fishing communities and the association of network structures with collaborative behavior of small-scale fisheries in the Northern Gulf of California, Mexico.I found considerable connectivity of communities via kinship ties of small-scale fishers, both within the region and to other areas in Mexico. Fisher kinship relationships are important mechanisms for information transfer. Identifying communities in the network that are most likely to share information with other communities allows managers to develop more effective and efficient education, outreach, and enforcement efforts.Communities are also connected by their use of the same fishing zones and Marine Protected Areas (MPAs). My results provide suggestions for dividing communities based on common use of fishing areas and MPAs. This may help fishers and managers to develop, implement, and enforce boundary rules that will facilitate regional management of small-scale fisheries. My results provided mixed evidence for the role of social structure in impacting positive outcomes for fisher' ability to collaborate and organize. A wide range of factors affect the emergence of institutions for CPR management. Similarly, finding a common network structure that can accurately predict sustainable use of CPRs is unlikely. Knowing how people are connected and the ways in which information about CPR resources moves through (or is hindered from moving through) a network can improve manager's ability to develop more effective strategies and actions. Adding social networks into the CPR management toolbox provides a mechanism by which those working in management and conservation can incorporate social structure into management activities.An understanding of the social networks that connect communities and the potential pathways for information transfer, combined with a system of enforceable rules and policies and effective outreach methods and materials, may help managers and resource users more effectively and sustainably manage CPRs in the long term.