Fair Copyright for Canada: Lessons for Online Social Movements from the First Canadian Facebook Uprising

Despite their growing importance, the political effectiveness of social media remains understudied. Drawing on and updating resource mobilization theory and political process theory, this article considers how social media make “political engagement more probable,” and the determinants of success for online social movements. It does so by examining the mainstreaming of the Canadian “user rights” copyright movement, focusing on the Fair Copyright for Canada Facebook page, created in December 2007. This decentralized, grassroots, social media-focused action – the first successful campaign of its kind in Canada and one of the first in the world – changed the terms of the Canadian copyright debate and legitimized Canadian user rights. As this case demonstrates, social media have changed the type and amount of resources needed to create and sustain social movements, creating openings for new groups and interests. Their success, however, remains dependent on the political context within which they operate.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Community Detection in Multi-Layer Networks

In the scope of the current thesis we review and analyse networks that are formed by nodes with several attributes. We suppose that different layers of communities are embedded in such networks, besides each of the layers is connected with nodes' attributes. For example, examine one of a variety of online social networks: an user participates in a plurality of different groups/communities – schoolfellows, colleagues, clients, etc. We introduce a detection algorithm for the above-mentioned communities. Normally the result of the detection is the community supplemented just by the most dominant attribute, disregarding others. We propose an algorithm that bypasses dominant communities and detects communities which are formed by other nodes' attributes. We also review formation models of the attributed networks and present a Human Communication Network (HCN) model. We introduce a High School Texting Network (HSTN) and examine our methods for that network.

Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.

An Exploration of Professors’ Use of Twitter in Higher Education

Scant research has explored how professors in Canadian universities use Twitter as a teaching tool or to augment knowledge about their subject disciplines. This case study employed a mixed-method approach to examine how professors in an Ontario university use Twitter. Using a variation of the technology acceptance model, the survey (n = 17) found that professor participants—41.2% of whom use Twitter—perceive Twitter as somewhat useful as a teaching tool, not useful for finding and sharing information, and not useful for personal use. Participants’ gender and number of years teaching are not indicators of Twitter use. Furthermore, the level of support from peers and the university may be reasons why some do not use Twitter or have stopped using Twitter. Face-to-face interviews (n = 3) revealed that Twitter is not used in classrooms or lecture halls, but predominantly as a means of sharing information with students and colleagues. Another deterrent to using Twitter is not knowing who to follow. Findings indicate that some professors at this university embrace Twitter, but not necessarily as an in-class teaching tool. The challenge and the advantage of using Twitter is to discover and follow people who tweet material and to select relevant material to pass along to students and colleagues. Professor participants in the study found a use for the social network as a means to increase student engagement, create virtual information-exchange communities, and enrich their own learning.

A Longitudinal Examination of Bidirectional Associations between Subjective Sleep Characteristics and Psychosocial Functioning among University Students

A number of studies have found a significant link between sleep and psychosocial functioning among university students. A critical examination of this literature, however, indicates that one important gap within the literature is the need for longitudinal studies that specifically test for bidirectional associations between these two constructs. The main purpose of my dissertation was to address this gap by conducting three studies that examined bidirectional associations between sleep and psychosocial functioning among a sample of university students. Participants were 942 (71.5% female) undergraduate students enrolled at a Canadian university, who completed survey assessments annually for three consecutive years, beginning in their first year of university. In the first study, I assessed bidirectional associations between two sleep characteristics (sleep quality and sleep duration) and three psychosocial functioning variables (academics, friendship quality, and intrapersonal adjustment). Results based on cross-lagged models indicated a significant bidirectional association between sleep quality and intrapersonal adjustment, such that more sleep problems predicted more negative intrapersonal adjustment over time, and vice versa. Unidirectional associations indicated that both higher academic achievement and more positive friendship quality were significant predictors of less sleep problems over time. In the second study, in which I examined bidirectional associations between sleep and media use, results provided support only for unidirectional associations; such that more sleep problems predicted increases in both time spent watching television and time spent engaged in online social networking. In the third study of my dissertation, in which I examined social ties at university and sleep quality, results indicated a significant bidirectional association, such that more positive social ties predicted less sleep problems over time, and vice versa. Importantly, emotion regulation was a significant mediator of this association. Findings across the three studies, highlight the importance of determining the direction of effects between different sleep characteristics and various aspects of university students’ psychosocial functioning, as such findings have important implications for both methodology and practice. A better understanding of the nature of the associations between sleep and psychosocial functioning will equip students, parents and university administrators with the tools necessary to facilitate successful adjustment across the university years.