Yes / Social media have become an important interaction channel between the government and citizens in the era of the digital community. The adoption of social media in local government services offers a new channel to encourage citizen engagement in the public policy decision-making process. Moreover, communication with citizens through social media exposes large opportunities for the local government to analyse and appreciate the relationships among social media participants in the digital community to enhance public services. The purpose of this study is to understand the local government’s social media network and identify the social role in the local government’s social media network structure. Thus, this study adopted the social network analysis (SNA) approach on the Twitter data of a local government’s official account in the UK as a case study. The study revealed that the internal local government stakeholders play an important social role in the local government’s social media network. The implication of the study was discussed.

This thesis represents a synthesis of relational learning and causal discovery, two subjects at the frontier of machine learning research. Relational learning investigates algorithms for constructing statistical models of data drawn from of multiple types of interrelated entities, and causal discovery investigates algorithms for constructing causal models from observational data. My work demonstrates that there exists a natural, methodological synergy between these two areas of study, and that despite the sometimes onerous nature of each, their combination (perhaps counterintuitively) can provide advances in the state of the art for both. Traditionally, propositional (or "flat") data representations have dominated the statistical sciences. These representations assume that data consist of independent and identically distributed (iid) entities which can be represented by a single data table. More recently, data scientists have increasingly focused on "relational" data sets that consist of interrelated, heterogeneous entities. However, relational learning and causal discovery are rarely combined. Relational representations are wholly absent from the literature where causality is discussed explicitly. Instead, the literature on causality that uses the framework of graphical models assumes that data are independent and identically distributed. This unexplored topical intersection represents an opportunity for advancement --- by combining relational learning with causal reasoning, we can provide insight into the challenges found in each subject area. By adopting a causal viewpoint, we can clarify the mechanisms that produce previously identified pathologies in relational learning. Analogously, we can utilize relational data to establish and strengthen causal claims in ways that are impossible using only propositional representations.

This dissertation aims to advance our understanding of workplace aggression by developing and testing two models based on the stereotype content model (SCM) and adopting the social network analysis approach. Specifically, two studies of the dissertation focus on (a) unfavorable social evaluations stemming from competence stereotypes, (b) stereotype-driven negative emotions as a mechanism to explain the relationship between competence and workplace aggression, and (c) the role of informal workplace relationships in predicting workplace aggression. Study one investigates the nonlinear relationship between job performance and exposure to workplace aggression and two distinct mediating mechanisms at high and low levels of job performance. High performers provoke jealousy, and low performers provoke contempt from coworkers, both of which are positively associated with exposure to workplace psychological aggression. The study tested these relations using data from a sample of 187 teachers from educational institutions in India and found support for the curvilinear relationship between performance and workplace psychological aggression and the mediating mechanisms of jealousy and contempt for high and low performers, respectively. Study two examines the role of workplace social ties (advice and friendship ties) in predicting workplace aggression. Results from data collected at 2-time points from 248 individuals in 21 workgroups largely supported the proposition that highly competent employees become victims of covert aggression and low competence employees become victims of overt aggression. Interestingly, the findings suggest that advice-giving and friendship ties mitigate the experience of aggression by reducing coworkers’ envy. However, advice-seeking aggravates overt aggression by increasing coworkers’ contempt. / Dissertation / Doctor of Philosophy (PhD)

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Performing social network analysis (SNA) requires a set of powerful techniques to analyze structural information contained in interactions between social entities. Many SNA technologies and methodologies have been developed and have successfully provided significant insights for small-scale interactions. However, these techniques are not suitable for analyzing large social networks, which are very popular and important in various fields and have special structural properties that cannot be obtained from small networks or their analyses. There are a number of issues that need to be further studied in the design of current SNA techniques. A number of key issues can be embodied in three fundamental and critical challenges: long processing time, large computational resource requirements, and network dynamism. In order to address these challenges, we discuss an anytime-anywhere methodology based on a parallel/distributed computational framework to effectively and efficiently analyze large and dynamic social networks. In our methodology, large social networks are decomposed into intra-related smaller parts. A coarse-level of network analysis is built based on comprehensively analyzing each part. The partial analysis results are incrementally refined over time. Also, during the analyses process, network dynamic changes are effectively and efficiently adapted based on the obtained results. In order to evaluate and validate our methodology, we implement our methodology for a set of SNA metrics which are significant for SNA applications and cover a wide range of difficulties. Through rigorous theoretical and experimental analyses, we demonstrate that our anytime-anywhere methodology is / Ph. D.

Spatial and temporal patterns of urban vegetation have been widely studied since the mid-twentieth century, but these studies have focused mainly on northern hemisphere countries, and little research has been conducted in developing countries. Urban vegetation is characterized by the presence of species that are adapted to the particular environmental conditions of cities, and a high diversity of exotic species. This occurs due to a combination of factors: on one hand, it is possible to find wild vegetation (weeds) on abandoned lands or those with little intervention, as well as on walls and buildings. On the other hand, there is also an enormous variety of ornamental and mainly exotic species, which have been cultivated by humans. The processes of species selection performed individually or collectively are a major determinant of the diversity of urban vegetation and flora. Individual decisions relate to private spaces such as residential gardens whose owners manage the vegetation according to their preferences and interests. Collective decisions relate to public spaces, which, by their nature, are subject to the action of multiple stakeholders. At the collective level, decision-making occurs in the context of processes involving local governments, other state agencies, NGOs and various interest groups present in the city. Each of these actors has its own vision on the role of urban vegetation, their preferences and criteria for the selection and management. This study aims to investigate the processes of decision-making responsible for the current composition of the vegetation in public spaces of the Metropolitan Area of Santiago (MAS). Through this research is expected to identify the criteria for the selection of species to be planted in public spaces, the reasons that explain the predominance of certain species, and the difference between parks managed by different public agencies in MAS. The research assumes that the various public and private actors involved in the planting and management of vegetation in public spaces, act guided by criteria and preferences that are finally expressed in the observed patterns of urban vegetation. For this purpose, the conceptual framework of governance is used, understood as the process of decision-making concerning public affairs, which involves multiple agents or interests including government agencies, non-governmental organizations and civil society groups. The overarching objectives of this Thesis are: a) To contribute to the knowledge of interactions between governance system and urban vegetation patterns in metropolitan areas of developing countries, recognizing both social and environmental processes interacting. b) Contribute to urban planning and policies by generating knowledge relevant to decision- making regarding urban vegetation. A robust knowledge of the factors defining the composition and structure of urban vegetation is essential to design effective policies for increasing vegetation cover, with consequent environmental and social benefits.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / The idea of socializing with other people of different backgrounds and cultures excites the web surfers. Today, there are hundreds of Social Networking sites on the web with millions of users connected with relationships such as "friend", "follow", "fan", forming a huge graph structure. The amount of data associated with the users in these Social Networking sites has resulted in opportunities for interesting data mining problems including friendship link and interest predictions, tag recommendations among others. In this work, we consider the friendship link prediction problem and study a topic modeling approach to this problem. Topic models are among the most effective approaches to latent topic analysis and mining of text data. In particular, Probabilistic Topic models are based upon the idea that documents can be seen as mixtures of topics and topics can be seen as mixtures of words. Latent Dirichlet Allocation (LDA) is one such probabilistic model which is generative in nature and is used for collections of discrete data such as text corpora. For our link prediction problem, users in the dataset are treated as "documents" and their interests as the document contents. The topic probabilities obtained by modeling users and interests using LDA provide an explicit representation for each user. User pairs are treated as examples and are represented using a feature vector constructed from the topic probabilities obtained with LDA. This vector will only capture information contained in the interests expressed by the users. Another important source of information that is relevant to the link prediction task is given by the graph structure of the social network. Our assumption is that a user "A" might be a friend of user "B" if a) users "A" and "B" have common or similar interests b) users "A" and "B" have some common friends. While capturing similarity between interests is taken care by the topic modeling technique, we use the graph structure to find common friends. In the past, the graph structure underlying the network has proven to be a trustworthy source of information for predicting friendship links. We present a comparison of predictions from feature sets constructed using topic probabilities and the link graph separately, with a feature set constructed using both topic probabilities and link graph.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / William H. Hsu / An ontology can be seen as an explicit description of the concepts and relationships that exist in a domain. In this thesis, we address the problem of building an interests' ontology and using the same to construct features for predicting both potential friendship relations between users in the social network Live Journal, and users' interests. Previous work has shown that the accuracy of predicting friendship links in this network is very low if simply interests common to two users are used as features and no network graph features are considered. Thus, our goal is to organize users' interests into an ontology (specifically, a concept hierarchy) and to use the semantics captured by this ontology to improve the performance of learning algorithms at the task of predicting if two users can be friends. To achieve this goal, we have designed and implemented a hybrid clustering algorithm, which combines hierarchical agglomerative and divisive clustering paradigms, and automatically builds the interests' ontology. We have explored the use of this ontology to construct interest-based features and shown that the resulting features improve the performance of various classifiers for predicting friendships in the Live Journal social network. We have also shown that using the interests' ontology, one can address the problem of predicting the interests of Live Journal users, a task that in absence of the ontology is not feasible otherwise as there is an overwhelming number of interests.