An individual’s alcohol use influences, and is influenced by, the drinking in their social network. Indeed, a person’s propensity to drink heavily is associated with having a social network in which more people drink heavily. This association has largely been reported in non-clinical samples of young adults and in networks of 10 or fewer relationships. Exploring social networks in older adults and in more distal relationships may further elucidate the interplay between an individual’s drinking and their social network. This dissertation explores the associations between a person’s social network and their drinking behaviour in adults, in clinical samples of people with alcohol use disorder (AUD), and in both proximal and distal social networks. The first study explores the psychometric properties of a brief egocentric (i.e., individual self-reports on their network) social network analysis (SNA) measure in general community adult drinkers. The second study compares a brief and a more extensive, or formal, SNA measure in terms of the capacity to identify AUD. The third study investigates the association between social networks and recovery from AUD, using a simple measure of social network alcohol use. Finally, the fourth study examines whether a higher-resolution SNA tool could provide a more in-depth understanding of the relationship between social networks and recovery from AUD. The results of this dissertation validate several uses of SNA in the context of AUD in adults: first, a brief measure demonstrates excellent psychometric properties. Second, both brief and formal SNA measures demonstrate the capacity to accurately classify those with and without AUD. Third, social networks play both a moderating and/or mediating role in AUD recovery, depending on the phase of recovery and type of social network measure used. Collectively, this dissertation provides an important foundation for further applications of SNA in clinical research and practice. / Thesis / Doctor of Philosophy (PhD) / An individual's social network plays a critical role in influencing behaviour, including their alcohol use. However, the majority of research investigating this association has focused on young adults in non-clinical samples and in close social circles. The purpose of this dissertation was to explore the role of an individual’s social networks on that individual’s drinking behaviour across a broad age range, in clinical samples, and in both immediate and extended social circles. The results suggest that social network alcohol use is consistently associated with an individual’s alcohol consumption. Beyond providing important insight into the social context of alcohol use, the findings indicate that measuring network influences could provide clinical value by potentially informing diagnostic assessments of alcohol use disorder and intervention approaches that focus on both the individual and their social context.

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.

The present thesis aims to deduce tasks and characteristics of end users during the open innovation process on the social web. The social web with its communities, forums and blogs affords new prospects as well as unknown challenges for companies, and at the same has increasingly influenced academic research during the last few years. Especially research regarding communication behaviour on the social web as well as social web technologies has currently progressed well. However, in innovation research, social web technologies are currently primarily used to integrate users into the company’s innovation process, for example through company user toolkits or company innovation communities. In those cases users were excluded from their normal social web environment and integrated into a company’s environment, a sort of laboratory environment. Despite this, the present research project will use the natural behaviour, comments and discussions of users within their social web environment to develop and apply a new mixed-method approach with the aim to deduce tasks and characteristics of innovative end users on the social web. To apply the mixed-method approach within a longitudinal case study and to deduce statements and regularities regarding the innovation process on the social web, it was possible to analyse the end user developer online forum of one of the leading open source CRM software technologies. Based on this analysis, the assumptions from an extensive literature analysis could be verified and extended: it could be shown that the expected single innovative user does not exist. In fact, the process from the initial idea to an innovation requires different users with different characteristics and different points of view. They will be deduced, explained and presented within the present thesis.

The UK is required to reduce its greenhouse gas emissions by 80 per cent from 1990 levels, by 2050. Greenhouse gas emissions attributed to the UK higher education sector have increased by 34.5 per cent from 1990 to 2005. Higher education institutions have a unique role in the UK greenhouse gas emissions inventory, beyond management of their own estates and compliance with policy and legislation, higher education institutions have responsibilities as innovators and educators, inspiring students and employees through example and best practice. This study sought to understand acceptability of greenhouse gas emissions reduction policies among employees of a higher education institution. The value-belief-norm theory was used in a questionnaire to understand individual attitudinal factors thought to influence policy acceptability (N=405). Recognising that an employee's attitudinal factors may be influenced by their work colleagues, this study used social network analysis to understand the social context within which individual attitudinal factors sit. Support was found for higher education institutions to reduce their greenhouse gas emissions. Employees found policies that encouraged desired behaviours, such as assistance with train travel costs and working from home, to be more acceptable than policies that discouraged undesired behaviours, such as doubling the price of a car-parking permit. Support was found for the structure and content of the value-belief-norm theory, but logistic regression suggested that it provided a weak explanation of employee policy acceptability, indicating that other factors may have a greater role. Analysis of workplace social networks suggested that employees have small social groups (x̄=8) and do not select to be close to colleagues that reflect their own perspectives. Practitioners and policymakers should seek to address this void in environmental social norms through recruitment of more environmental champions to deliver strong and persuasive pro-environmental messages.

The innovation ecosystem’s online presence continues to grow with the emergence and maturation of ICT-based platforms. With these new channels, a diversity of actors, including firms, scientists, universities, media entities, and individuals, interact to satisfy their information needs and to access and mobilize network-based resources. This research is among a growing number of social science studies examining the advent of social media and its influence on the innovation process, asking, “How do different types of actors use social media to form network linkages, and what kinds of innovative outcomes will result?” To study this complex network activity, I turn to Twitter, the popular microblogging service, and focus on the case of graphene, a novel nanotechnology material consisting of a two-dimensional sheet of carbon atoms. Twitter is one of the world’s most often-used social networks, boasting over 500 million users (200+ million active). Graphene, on the other hand, is a relatively well-bounded area of scientific inquiry with ongoing, concurrent scientific and commercialization activity. The primary sample dataset derives from 34 graphene firms’ friend and followers relationships captured in early 2014. Nine interview transcripts supply qualitative data. The results show that network formation on Twitter is not random and that certain actor relationships predict following linkages. A series of network visualizations show that users agglomerate in communities; these communities exhibit greater density than the larger ecosystem network and a propensity to congeal in topically focused ways. That is, each community indicates a coherent topical focus, suggesting that graphene firms follow specific sets of users in ways that support their information and resource needs. At the micro-level, an unstructured text mining approach to operationalizing and computing information distance shows that increasing amounts of topical distance between any two users decreases the likelihood of a tie existing. Are innovation outcomes more likely to occur in strategically-developed and information-rich social media networks? Drawing on different sources of “behavioral additionality” – or changes in behaviors as a result of social media participation – I identify ex-ante several such plausible outcomes, which could include increased awareness, improved problem solving ability, community development, and greater sales. The qualitative results show that social media participation results in increased awareness of graphene and related ecosystem topics, but engagement is a key tactical maneuver that actors pursue, often in varying ways, to access and mobilize other resources. Policy implications are targeted at intermediary institutions and scientists, while management implications focus on high-technology SMEs. Limitations include alternative theories to explaining social media participation and engagement, methodological issues, and the continuing evolution of social media platforms and usage patterns. Future work is considered to address the temporal nature of network construction and topical growth (or constriction), as well as the ability to map areas of science and technology through social media data.

This dissertation uses a relational approach and a contentious politics framework to examine the archaeological record. Methodologically, it merges spatial and social network analyses to promote a geosocial archaeology. Combined, the articles create a counter-narrative that highlights how environmentally focused investigations fail to explain how and why societies in the Southwest often reorganize horizontally. The first article uses geosocial networks, which I argue represent memory maps, to reveal that the socially important, and sophisticated, act of forgetting was employed by people in the Gallina region during A.D. 1100 - 1300. A concomitant community level, settlement pattern analysis demonstrates similarities between the arrangement of Gallina and Basketmaker-era settlements. These historically situated settlement structures, combined with acts of forgetting, were used by Gallina region residents to institute and maintain a horizontally organized social movement that was likely aimed at rejecting the hierarchical social atmosphere in the Four Corners region. The second article proposes that as ideologically charged material goods are consumed, fissures within past ideological landscapes are revealed and that these fissures can demonstrate acts of resistance in the archaeological past. It also contends that social and environmental variables need to be combined for these conflicting religious and political practices to be correctly interpreted. The third article applies many of the ideas outlined in the second article to a case study in the Greater Southwest during A.D. 1200 - 1450. Fractures in the ideological landscape demonstrate that the Salado Phenomenon was a religious social movement formed around, and successful because of, its populist nature. Based on variations in how the Salado ideology interacted with contemporaneous hierarchical and non-hierarchical religious and political organizations it is probable that the Salado social movement formed around desires for the open access to religious knowledge.

Thesis (PhD)--University of Stellenbosch, 2004. / ENGLISH ABSTRACT: Please refer to full text for abstract.

published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

Job seekers and employers frequently make application and selection decisions based on how well they believe there is a ‘fit’ with the organization and job. The personenvironment fit literature has strongly supported this practice demonstrating that fit is an antecedent to attraction, selection, and attrition. What has been lacking, however, is evidence that once individuals enter the organization their fit relates to performance. Using a social network analytical lens, I develop a framework that integrates PE fit and social networks to explore antecedents to employee performance. Using this framework, I explore how informal workplace relationships may act as catalysts through which fit either enhances or detracts from individual performance, how fit might directly influence performance once the social context is taken into account, and how fit might make an individual an attractive exchange partner benefiting performance. Results suggest that PE fit is related to individual performance (both in- and extra-role) but that this relationship differs depending on how well embedded the employee is in the informal social networks of the organization. It is only when accounting for the ‘who you know’ element of organizational life that we can see how ‘who you are’ relates to performance.