Analyzing Key Users' behavior trends in Volunteer-Based Networks

Online social networks usage has increased significantly in the last decade and continues to grow in popularity. Multiple social platforms use volunteers as a central component. The behavior of volunteers in volunteer-based networks has been studied extensively in recent years. Here, we explore the development of volunteer-based social networks, primarily focusing on their key users' behaviors and activities. We developed two novel algorithms: the first reveals key user behavior patterns over time; the second utilizes machine learning methods to generate a forecasting model that can predict the future behavior of key users, including whether they will remain active donors or change their behavior to become mainly recipients, and vice-versa. These algorithms allowed us to analyze the factors that significantly influence behavior predictions. To evaluate our algorithms, we utilized data from over 2.4 million users on a peer-to-peer food-sharing online platform. Using our algorithm, we identified four main types of key user behavior patterns that occur over time. Moreover, we succeeded in forecasting future active donor key users and predicting the key users that would change their behavior to donors, with an accuracy of up to 89.6%. These findings provide valuable insights into the behavior of key users in volunteer-based social networks and pave the way for more effective communities-building in the future, while using the potential of machine learning for this goal

USER PROFILING AND PRIVACY PRESERVING FROM MULTIPLE SOCIAL NETWORKS

Ph.DDOCTOR OF PHILOSOPH

The Impact of Digital Technology on Environmental Sustainability and Resilience: An Evidence Review

Striving for ‘environmental sustainability and resilience’ (ESR) is postulated as a crucial, universal and global challenge of the twenty-first century. Today, this challenge has to be addressed in a world that is dynamic in its societal, economic and political constituents, heightened by increased interconnectedness resulting from globalisation. From a developing country perspective these issues need to be reconciled alongside developmental priorities, producing ongoing controversies and contradictions. This is further compounded by the fundamental matter of climate change. Undeniably the multitude of dimensions interlinked to achieving ESR are inherently complex and dynamic, inter-related across geographies of scale, space and place. Hence recent academic literature depicts the necessity of a systems-based approach in effectively conceptualising the field in the future. It is proposed that to account for the competing challenges and complexity, radical rethinking and innovation of approaches are required. This standpoint contrasts strongly with conventional development approaches, which predominantly focus on ‘palliative care’. One area from which relevant innovation stems is the digital sector. As this is now firmly what many term the ‘digital age’ or even ‘digital revolution’, there has recently been increasing application of digital technologies in developing country contexts. To date, this innovation has incorporated a spectrum of developmental initiatives, inclusive of those categorised under ESR headings. Inevitably, evidence on the impact of these innovations in practice is key in justifying resources, continuous learning and enabling effective progress. The latest World Development Report, Digital Dividends, explores impact from a development perspective focused largely on increased prosperity and inclusivity through economic growth, social dynamics and public service delivery in developing countries. Similarly, there are examples such as the UK Department for International Development’s recent review exploring the impact of digital technologies across their own development programmes. However, there appears to be relatively limited work to date collating and addressing the existing evaluative evidence specifically from the ESR viewpoint. The evidence that does currently exist on these areas seems to be primarily documented at the individual intervention level. The objective of this report was, therefore, to contribute to this evolving field by exploring and synthesising existing documented evidence. Commonalities and disjoints of successes and failures were drawn from across the findings, to produce a stronger evidence base on the impact of digital innovation in ESR. Impacts were considered alongside the academic theorisations of innovation with particular reference to complexity. This aims to identify emerging themes and gaps to ultimately deduce research-informed policy and to practice relevant lessons moving forward.UK Department for International Developmen

Wellness Representation of Users in Social Media: Towards Joint Modelling of Heterogeneity and Temporality

The increasing popularity of social media has encouraged health consumers to share, explore, and validate health and wellness information on social networks, which provide a rich repository of Patient Generated Wellness Data (PGWD). While data-driven healthcare has attracted a lot of attention from academia and industry for improving care delivery through personalized healthcare, limited research has been done on harvesting and utilizing PGWD available on social networks. Recently, representation learning has been widely used in many applications to learn low-dimensional embedding of users. However, existing approaches for representation learning are not directly applicable to PGWD due to its domain nature as characterized by longitudinality, incompleteness, and sparsity of observed data as well as heterogeneity of the patient population. To tackle these problems, we propose an approach which directly learns the embedding from longitudinal data of users, instead of vector-based representation. In particular, we simultaneously learn a low-dimensional latent space as well as the temporal evolution of users in the wellness space. The proposed method takes into account two types of wellness prior knowledge: (1) temporal progression of wellness attributes; and (2) heterogeneity of wellness attributes in the patient population. Our approach scales well to large datasets using parallel stochastic gradient descent. We conduct extensive experiments to evaluate our framework at tackling three major tasks in wellness domain: attribute prediction, success prediction, and community detection. Experimental results on two real-world datasets demonstrate the ability of our approach in learning effective user representations

A moment to give, no moment to take : a mixed-methods study on volunteer tourism

The number of research projects (theses and non-theses) on volunteer tourism (VT) has increased significantly over the last twelve years. However, few studies have measured an individual’s intention and stage of readiness to be involved in VT. The main purpose of this thesis is to develop a model to predict intention and stage of readiness to be involved in volunteer tourism by applying variables, including attitudes towards the concept of volunteer tourism (VT), subjective norms, altruistic lifestyle values, the sensation-seeking personality, social class, taking/receiving motivation, giving motivation, past experience in tourism, past experience in volunteering, past experience in VT, perceived and factual constraints. This study was conducted using a sequential mixed methods approach. A qualitative approach was utilised in the first stage. Qualitative data were gathered via focus groups (15 participants), in-depth interviews (15 participants), and email interviews (three participants) which were conducted in Perth, Western Australia. The participants selected were those who had experience as a volunteer tourist and/or as an international volunteer. The data were analysed using NVivo (Welsh, 2002a) version 10. The second stage consisted of a quantitative study and collected data via an online survey. This survey attracted 542 participants with a range of experience in VT. The participants mainly resided in Australia. Data were analysed using the Statistical Package for the Social Sciences (SPSS) (Allen & Bennett, 2010; Blunch, 2008; Coakes, Steed, & Ong, 2010; Pallant, 2010) for the exploratory factor analysis and AMOS (Analysis of Moment Structures) for confirmatory factor analysis and structural equation modelling (Blunch, 2008; Byrne, 2001; Hair Jr., Black, Babin, Anderson, & Tatham, 2006; Holmes-Smith, 2010). As a result of reviewing the literature on VT (Broad & Jenkins, 2008; Hobbs, 2007; K. D. Lyons & Wearing, 2012; E. Raymond, 2008; Tourism Research and Marketing, 2008; Wearing, 2001) and the findings of both the qualitative and quantitative studies, volunteer tourism was conceptualised as the intersection of volunteer and tourism concepts and a combination of volunteer and tourism activities in a travel destination (nationally or internationally). This requires motivated participants to volunteer for a day or more and pay their own costs, for instance, for transport, accommodation, meals, and in some instances financially contribute to the project (these could include humanitarian, education, health, construction, religion, and conservation projects) itself, through a sender or host organisation. In the qualitative study, it was found that people engaged in VT for a variety of reasons and had varied VT experiences. A key factor with participants was the importance of word of mouth influences in inspiring and encouraging their involvement in VT. This was found to be a powerful motivator. Broadly the experiences ranged from religious, to embracing the environment, to adventure seeking, to being free and genuinely altruistic motivations. Constraints to be involved in VT came from participants with more limited VT experience and included time, money, distance and language difficulties. These findings were applied in developing the research instrument by adding, confirming, and modifying the information taken from the literature review in tourism, volunteerism, and VT. In the quantitative study, modifications were made to the initial conceptual framework proposed, for example, by dividing the motivation variable into ‘taking/receiving motivation’ and ‘giving motivation’ to ensure that drivers of each type of motivation were explored. Using a structural equation model, the relationship between these two different types of motivation and their impact on intention was examined. To measure individuals’ intentions and stage of readiness to be involved in VT, six models were developed, each individually testing a research question. A key driver for predicting intention to engage in VT was the ‘giving motivation’; this included the need to give back to the community and a concern for the environment. In addition, a group of other key drivers included attitudes, subjective norms, ‘taking/receiving motivation’ and a sensation-seeking personality. These findings both added to and consolidated exiting literature on the intention to engage in VT. Findings indicated a strengthening of intention as time increased (from 1 to 3 to 5 years) along with different drivers impacting the decision at different time intervals. This information is critical to marketers and social marketers of VT so as to effectively communicate with potential volunteer tourists with time appropriate messages. Further, this study of individuals’ intention and stage of readiness to be involved in VT is significant to tourism, volunteerism, and VT knowledge, obviously seen from the qualitative study. Individuals might be easy to be just a tourist, travel to a city or country for any purpose. However, to add volunteering activity, such as working in a community or orphanage, in the destination might attract only certain people. Most participants needed a proper and long preparation before they were really involved in VT. For non-volunteer tourists, some disagreed to the concept of VT and some others even though they agreed to the concept, they considered later in their lives if they wanted to be involved in VT. The findings indicate that VT is still an alternative, niche, and special tourism product as VT requires a high involvement from each participant

Making the Case for Place: An Exploration of Urbanization Measures on a Model of Social Capital and U.K. Crime Rates

Studies of social capital and crime have become quite popular in recent history, and a plethora of empirical tests have sought to clarify relationships between the two variables. However, most of these studies center on communities in the United States, and often overlook the many differentiating features between urban and rural communities that would affect such models. Reasons offered for such skew in the past and current research on this subject are middling at best, and largely cite either a lack of availability in data for crime and social capital in non-urban communities, or questionable accuracy for what data is accessible. This dissertation sought to address both the lack of research on social capital effects on crime rates in communities outside of the U.S., and the lack of consideration of urbanization level in such research. Hypotheses derived under these general goals were tested using a combination of multivariate regression analyses and structural equation modeling on datasets provided by the Office of National Statistics (U.K.) and the British Social Attitudes Survey. Findings revealed social capital and crime models vary between urban and rural communities. It was also revealed that models of social capital and crime are contingent upon crime type and urbanization level. Conclusions and implications from this research suggested social capital is relevant in social capital-crime discourse in the U.K., but not always in the ways that current literature suggests it would be. Additionally, it was clear that greater specificity in social capital-crime models in the U.K. is warranted as the data revealed such models are only relevant for a limited combination of crime and community types. Future research should expand towards clarifying the relationship between social capital and crime rates in rural U.K. areas, incorporate more definitions of social capital driven by the idiosyncratic features of urban and rural communities, and consider more exploration of these models in countries typically underrepresented in the literature

WELLNESS PROFILING ON SOCIAL NETWORKS

Ph.DDOCTOR OF PHILOSOPH