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Bordianu, Gheorghita. "Learning influence probabilities in social networks." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114597.
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Social network analysis is an important cross-disciplinary area of research, with applications in fields such as biology, epidemiology, marketing and even politics. Influence maximization is the problem of finding the set of seed nodes in an information diffusion process that guarantees maximum spread of influence in a social network, given its structure. Most approaches to this problem make two assumptions. First, the global structure of the network is known. Second, influence probabilities between any two nodes are known beforehand, which is rarely the case in practical settings. In this thesis we propose a different approach to the problem of learning those influence probabilities from past data, using only the local structure of the social network. The method is grounded in unsupervised machine learning techniques and is based on a form of hierarchical clustering, allowing us to distinguish between influential and the influenceable nodes. Finally, we provide empirical results using real data extracted from Facebook.L'analyse des réseaux sociaux est un domaine d'études interdisciplinaires qui comprend des applications en biologie, épidémiologie, marketing et même politique. La maximisation de l'influence représente un problème où l'on doit trouver l'ensemble des noeuds de semence dans un processus de diffusion de l'information qui en même temps garantit le maximum de propagation de son influence dans un réseau social avec une structure connue. La plupart des approches à ce genre de problème font appel à deux hypothèses. Premièrement, la structure générale du réseau social est connue. Deuxièmement, les probabilités des influences entre deux noeuds sont connues à l'avance, fait qui n'est d'ailleurs pas valide dans des circonstances pratiques. Dans cette thèse, on propose un procédé différent visant la problème de l'apprentissage de ces probabilités d'influence à partir des données passées, en utilisant seulement la structure locale du réseau social. Le procédé se base sur l'apprentissage automatique sans surveillance et il est relié à une forme de regroupement hiérarchique, ce qui nous permet de faire la distinction entre les noeuds influenceurs et les noeuds influencés. Finalement, on fournit des résultats empiriques en utilisant des données réelles extraites du réseau social Facebook.
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Sharad, Kumar. "Learning to de-anonymize social networks." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/262750.
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Releasing anonymized social network data for analysis has been a popular idea among data providers. Despite evidence to the contrary the belief that anonymization will solve the privacy problem in practice refuses to die. This dissertation contributes to the field of social graph de-anonymization by demonstrating that even automated models can be quite successful in breaching the privacy of such datasets. We propose novel machine-learning based techniques to learn the identities of nodes in social graphs, thereby automating manual, heuristic-based attacks. Our work extends the vast literature of social graph de-anonymization attacks by systematizing them. We present a random-forests based classifier which uses structural node features based on neighborhood degree distribution to predict their similarity. Using these simple and efficient features we design versatile and expressive learning models which can learn the de-anonymization task just from a few examples. Our evaluation establishes their efficacy in transforming de-anonymization to a learning problem. The learning is transferable in that the model can be trained to attack one graph when trained on another. Moving on, we demonstrate the versatility and greater applicability of the proposed model by using it to solve the long-standing problem of benchmarking social graph anonymization schemes. Our framework bridges a fundamental research gap by making cheap, quick and automated analysis of anonymization schemes possible, without even requiring their full description. The benchmark is based on comparison of structural information leakage vs. utility preservation. We study the trade-off of anonymity vs. utility for six popular anonymization schemes including those promising k-anonymity. Our analysis shows that none of the schemes are fit for the purpose. Finally, we present an end-to-end social graph de-anonymization attack which uses the proposed machine learning techniques to recover node mappings across intersecting graphs. Our attack enhances the state of art in graph de-anonymization by demonstrating better performance than all the other attacks including those that use seed knowledge. The attack is seedless and heuristic free, which demonstrates the superiority of machine learning techniques as compared to hand-selected parametric attacks.
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Rogers, Brian W. Palfrey Thomas R. "Learning and status in social networks /." Diss., Pasadena, Calif. : Caltech, 2006. http://resolver.caltech.edu/CaltechETD:etd-05262006-004112.
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Milán, Pau. "The Social economics of networks and learning." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/393733.
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This thesis explores various economic environments where the structure of social interactions across individuals determines outcomes. In the first chapter, I study mutual insurance arrangements restricted on a social network. I test the network-based sharing rules on data from Bolivian communities, and I argue that this framework provides a reinterpretation of the standard risk sharing results, predicting household heterogeneity in response to income shocks. In the second paper, I study individual and collective behavior in coordination games where information is dispersed through a network. I show how changes in the distribution of connectivities in the population affect the types of coordination in equilibrium as well as the probability of success. In the third chapter, I explore a framework of learning and turnover in the labor market. I show that positive assortative matching (PAM) extends beyond the stable environment of Eeckhout & Weng (2010) to a situation of residual uncertainty that exhibits periods of unlearning. I also extend this setting to allow for career concerns and I show that PAM can only be sustained under strong assumptions.Esta tesis explora diversos entornos económicos en los que la estructura de las interacciones sociales entre los individuos determina los distintos resultados. En el primer capítulo, se estudia acuerdos de seguro mutuo restringidos en una red social. Utilizo datos de comunidades bolivianas para medir las predicciones teóricas y encuentro que los intercambios observados entre los hogares coinciden con la regla de reparto basada en la red obtenida por la teoría. Sostengo que este marco ofrece una reinterpretación de los resultados estándar de distribución de riesgos, prediciendo heterogeneidad entre los hogares en respuesta a los shocks de ingresos. En el segundo artículo, estudio el comportamiento individual y colectivo en juegos de coordinación, donde la información se dispersa a través de una red. Demuestro cómo los cambios en la distribución de las conectividades de la población afectan a los tipos de coordinación en equilibrio, así como la probabilidad de éxito. En el tercer capítulo, analizo un marco de aprendizaje y cambio de personal en el mercado de trabajo. Muestro que emparejamiento selectivo positivo (PAM) se extiende más allá del entorno estable de Eeckhout y Weng (2010) a una situación de incertidumbre residual que exhibe períodos de des-aprendizaje. También extiendo esta configuración para permitir elementos de career concerns y muestro que el equilibrio de PAM sólo puede sostenerse bajo fuertes supuestos.
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Lobel, Ilan. "Social networks : rational learning and information aggregation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54232.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 137-140).
This thesis studies the learning problem of a set of agents connected via a general social network. We address the question of how dispersed information spreads in social networks and whether the information is efficiently aggregated in large societies. The models developed in this thesis allow us to study the learning behavior of rational agents embedded in complex networks. We analyze the perfect Bayesian equilibrium of a dynamic game where each agent sequentially receives a signal about an underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). We characterize equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning -- that is, the conditions under which, as the social network becomes large, the decisions of the individuals converge (in probability) to the right action. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of expansion in observations. This result therefore establishes that, with unbounded private beliefs, there will be asymptotic learning in almost all reasonable social networks. Furthermore, we provide bounds on the speed of learning for some common network topologies. We also analyze when learning occurs when the private beliefs are bounded.
(cont.) We show that asymptotic learning does not occur in many classes of network topologies, but, surprisingly, it happens in a family of stochastic networks that has infinitely many agents observing the actions of neighbors that are not sufficiently persuasive. Finally, we characterize equilibria in a generalized environment with heterogeneity of preferences and show that, contrary to a nave intuition, greater diversity (heterogeneity) 3 facilitates asymptotic learning when agents observe the full history of past actions. In contrast, we show that heterogeneity of preferences hinders information aggregation when each agent observes only the action of a single neighbor.
by Ilan Lobel.
Ph.D.
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Oddone, Kay. "Teachers' experience of professional learning through personal learning networks." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/127928/1/Kay_Oddone_Thesis.pdf.
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There is an urgent need to improve continuing professional learning for teachers as education becomes increasingly complex. Traditional models of professional development are often fragmented, discrete events, disconnected from teachers' practice and perceived as empty measures of compliance. There is limited research that investigates alternative professional learning approaches that leverage online social technologies and involve teacher agency, collaboration and active participation. Therefore, this research explores teachers' experience of professional learning through personal learning networks (PLNs). The findings have supported the development of a new model of learning as a connected professional, which makes a significant contribution to theory and practice in the emerging field of professional networks and learning, enabled through the affordances of social technologies.
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de, Albuquerque Melo Cassio. "Scaffolding of self-regulated learning in social networks." Universidade Federal de Pernambuco, 2010. https://repositorio.ufpe.br/handle/123456789/2223.
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Previous issue date: 2010Conselho Nacional de Desenvolvimento Científico e Tecnológico
Scaffoldings são apoios a aprendizes novatos através de uma simplificação do contexto de aprendizagem. Estes apoios são gradualmente removidos à medida que os alunos desenvolvem estratégias autônomas de aprendizagem (processo conhecido como fading ). Em ambientes de aprendizagem online, os scaffoldings podem ser implementados através de um conjunto de funcionalidades que promovam o planejamento de objetivos, auto-monitoramento, auto-avaliação, estratégias de aprendizado, procura de ajuda, e planejamento e gerenciamento do tempo. Enquanto scaffoldings do Aprendizado Auto- Regulado (AAR) têm sido discutidos em ambientes tradicionais de aprendizagem, as redes sociais online têm pouca ou nenhuma atenção neste domínio. O presente estudo é focado em scaffoldings do AAR em redes sociais, pois acreditamos que as redes sociais têm estilos de interação que influenciam mais notadamente as habilidades individuais e coletivas do AAR. Nós coletamos itens do AAR no estado-da-arte sobre metacognição e aprendizagem, definimos suas metas e sugerimos scaffoldings para o AAR em redes sociais. Cada item foi extraído a partir de vários estudos na literatura sobre Computer-Supported Collaborative Learning (CSCL) e o AAR; dados quantitativos e qualitativos a partir de relatórios; estudos de caso; questionários AAR e outros recursos mencionados ao longo deste trabalho. Nós implementamos os mecanismos de scaffoldings na rede social Rede Social Educacional (Redu). Redu oferece um espaço de trabalho compartilhado, onde os alunos são incentivados a publicar os seus documentos e notas de aula, enquanto o professor fornece documentos e faz comentários para a classe. Os mecanismos de scaffoldings sugeridos incluem: 1) Blogs, comentários e fórum; 2) Instruções sobre tarefas, 3) Ajuda contextual e políticas de uso; 4) Perguntas para reflexão; 5) Fluxo de atividades; 6) Criação e compartilhamento de recursos; 7) Perfil de aprendizagem, 8) Notas de aula; 9) Discussões e assitência par-a-par; 10) Exames formativos; 11) Feedback de desempenho e orientação; 12) Mecanismos de recompensa e; 13) Visualização de informação. Em resumo, este trabalho sugere que uma rede social de aprendizagem pode ser concebida para melhorar o aprendizado auto-regulado através de mecanismos de scaffoldings apropriados
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Fidalgo, Patrícia Seferlis Pereira. "Learning networks and moodle use in online courses: a social network analysis study." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8862.
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Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e FormaçãoThis research presents a case study on the interactions between the participants of the forums of four online undergraduate courses from the perspective of social network analysis (SNA). Due to lack of studies on social networks in online learning environments in higher education in Portugal we have choose a qualitative structural analysis to address this phenomenon. The context of this work was given by the new experiences in distance education (DE) that many institutions have been making. Those experiences are a function of the changes in educational paradigms and due to a wider adoption of Information and Communication Technologies (ICT) from schools as well as to the competitive market. Among the technologies adopted by universities are the Learning Management Systems (LMSs) that allow recording, storing and using large amounts of relational data about their users and that can be accessed through Webtracking. We have used this information to construct matrices that allowed the SNA. In order to deepen knowledge about the four online courses we were studying we have also collect data with questionnaires and interviews and we did a content analysis to the participations in the forums. The three main sources of data collection led us to three types of analysis: SNA, statistical analysis and content analysis. These types of analysis allowed, in turn, a three-dimensional study on the use of the LMS: 1) the relational dimension through the study of forums networks and patterns of interaction among participants in those networks, 2) the dimension relative to the process of teaching and learning through content analysis of the interviews; 3) and finally the dimension related to the participants' perceptions about the use of LMS for educational purposes and as a platform for creating social networks through the analysis of questionnaires.With the results obtained we carried out a comparative study between the four courses and tried to present a reflection on the Online Project of the University as well as possible causes that led to what was observed. We have finished with a proposal of a framework for studying the relational aspects of online learning networks aimed at possible future research in this area.
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Laghos, Andrew. "Assessing the evolution of social networks in e-learning." Thesis, City University London, 2007. http://openaccess.city.ac.uk/8504/.
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This research provides a new approach to analysing the evolutionary nature of social networks that are formed around computer-mediated-communication (CMC) in e-Learning courses. Aspects that have been studied include Online Communities and student communication e-Learning environments. The literature review performed identified weaknesses in the current methods of analyzing CMC activity. A proposed unfied analysis framework (FESNeL) was developed which enables us to explore students' interactions and to test a number of hypotheses. The creation of the framework is discussed in detail along with its major components (e.g. Social Network Analysis and Human Computer Interaction techniques). Furthermore this framework was tested on a case study of an online Language Learning Course. The novelty of this study lies in the investigation of the evolution of online social networks, filling a gap in current research which focuses on specific time stamps (usually the end of the course) when analysing CMC. In addition, the framework uses both qualitative and quantitative methods allowing for a complete assessment of such social networks. Results indicate that FESNeL is a useful methodological framework that can be used to assess student communication and interaction in web-based courses. In addition, through the use of this framework, several characteristic hypotheses were tested which provided useful insights about the nature of learning and communicating online.
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Harris, Lisa, and Lisa Harris@rmit edu au. "Electronic Classroom, Electronic Community: Virtual Social Networks and Student Learning." RMIT University. Global Studies, Social Science and Planning, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080717.144715.
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The capacity for online learning environments to provide quality learning experiences for students has been the focus of much speculation and debate in the higher education sector from the late 1990s to the present day. In this area, 'quality' has become synonymous with engaging students in a learning community. This study reports on a qualitative research project designed to explore the significance of community for students when they study in online learning environments. This project used three case studies to explore tertiary students' thoughts and expectations about community in the online environment. The research was constructed iteratively. Data from the initial case suggested the need to explore the relationship between the constructed online learning environment and the development of learning communities or what I have termed Social Learning Support Networks (SLSN). To explore this issue further, the project was expanded and subsequent cases were chosen that included fundamentally different types of online learning environments. The project had two significant results. Firstly, students not only confirmed popular educational theories on the value of learning communities, but also described how this form of social connection might practically benefit their learning. Secondly, the project found that certain forms of synchronous online environments provided enhanced opportunities for students to form social connections that supported their learning. This project provides new evidence of the benefit of community for students studying online and argues that future online learning environments should be shaped by five key principles designed to foster a sense of social connection between students.
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Khurshid, Imran, and Maciej Twardowski. "Interorganizational Networks as Emerging Learning Organizations." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-22346.
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As the topic of sustainability is gaining a lot of importance, organizations in the aviation industry are coming together to form networks. The purpose of the study is to understand the concept of inter-organizational networks as potential learning organizations and find out how facilitating processes that enable these inter-organizational networks like collaboration, communication and knowledge management operate within networks. Further research will explore processes of learning in networks to investigate alignment and resemblance with the concept of sustainable learning organization and provide an insight on organization structure and culture as enablers of learning. The basic design of the study consists of semi-structured interviews of two networks in the aviation industry as primary data in order to support research questions with empirical analysis. In addition, systematic review of academic literature and official websites of various network stakeholders was used as a secondary data collection source to discover track record of current research study in this field and identify knowledge gaps and areas for further study. Major findings include impact of formal and informal structure of networks on learning processes and objective setting for the network. It also depicts a need for a holistic and systematic approach at interorganizational level in order to form a learning organization. In the end culture was also identified as a learning enabler in networks that tend to be sustainable learning organizations.
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Isaacs, Lorraine Ann. "Social constructivism and collaborative learning in social networks: the case of an online masters programme in adult learning." Thesis, University of the Western Cape, 2013. http://hdl.handle.net/11394/5130.
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Magister Educationis (Adult Learning and Global Change) - MEd(AL)This study investigates how students in an online Masters Programme in Adult Learning, although geographically dispersed used SNs to develop a supportive environment that enables collaborative learning to support and deepen their learning. Web 2.0 social software provided the tools for various forms of communication and information sharing amongst student within the social networks. This study shows how the use of Web 2.0 tools such as wikis, podcasts, blogs, chat rooms, social networking sites and email have the potential to expand the learning environment, increase participation and enrich the learning experience. Rapid technological developments transform our world into a global society which is ever changing and interconnected. The SNs as a learning environment in this technological driven global society is complex and not clearly defined; therefore it was not easy for me to understand the nature of the SNs as learning environment. The social nature of this study has therefore urged me to use social constructivism as a conceptual framework to gain insights into how students have used the social networks to develop a supportive environment that enables collaborative learning to support and deepen their learning. The utilisation of social constructivism as theoretical lens has helped to broaden my perceptions of the SNs as learning environment, to deepen my understanding of how learning occurs in the SNs and to comprehend learner behaviour within this pedagogical space. Social constructivists view learning as a social process in which people make sense of their world by interacting with other people (Doolittle & Camp, 1999). Social constructivists belief in the social nature of knowledge, and the belief that knowledge is the result of social interaction and language usage, and, thus, is a shared, rather than an individual, experience (Prawat & Floden, 1994). Furthermore, they believe that this social interaction always occurs within a socio-cultural context, resulting in knowledge that is bound to a specific time and place (Vygotsky, 1978).
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Lam, Ho-Yu. "A learning approach to spam detection based on social networks /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20LAM.
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Kent, Daniel N. "Essays on Machine Learning in International Conflict and Social Networks." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594487914627131.
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Arnell, Matilda, and Yuliya Bilinskaya. "Business opportunity creation through Social Networking Sites : A network perspective." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-179833.
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Mina, Christakis. "Open Technological Standardization Processes Through Learning Networks." 京都大学 (Kyoto University), 2010. http://hdl.handle.net/2433/120839.
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Skyring, Carol A. "Learning in 140 characters : microblogging for professional learning." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/65854/1/Carol_Skyring_Thesis.pdf.
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This thesis investigated how microblogging, a form of online social networking, was being employed by educators to support their professional learning. The study found that educators who participate in microblogging engage in a wide range of behaviours, with certain behaviours and activities commonly exhibited. An advantage of microblogging as a professional learning tool is its ability to link educators globally to exchange ideas from different perspectives and to share resources and teaching practices. Educators who microblog have access to relevant and timely learning that is not constrained by time or distance and can be tailored to meet their individual needs.
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Ezzeddine, Diala. "A contribution to topological learning and its application in Social Networks." Thesis, Lyon 2, 2014. http://www.theses.fr/2014LYO22011/document.
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L'Apprentissage Supervisé est un domaine populaire de l'Apprentissage Automatique en progrès constant depuis plusieurs années. De nombreuses techniques ont été développées pour résoudre le problème de classification, mais, dans la plupart des cas, ces méthodes se basent sur la présence et le nombre de points d'une classe donnée dans des zones de l'espace que doit définir le classifieur. Á cause de cela la construction de ce classifieur est dépendante de la densité du nuage de points des données de départ. Dans cette thèse, nous montrons qu'utiliser la topologie des données peut être une bonne alternative lors de la construction des classifieurs. Pour cela, nous proposons d'utiliser les graphes topologiques comme le Graphe de Gabriel (GG) ou le Graphes des Voisins Relatifs (RNG). Ces dernier représentent la topologie de données car ils sont basées sur la notion de voisinages et ne sont pas dépendant de la densité. Pour appliquer ce concept, nous créons une nouvelle méthode appelée Classification aléatoire par Voisinages (Random Neighborhood Classification (RNC)). Cette méthode utilise des graphes topologiques pour construire des classifieurs. De plus, comme une Méthodes Ensemble (EM), elle utilise plusieurs classifieurs pour extraire toutes les informations pertinentes des données. Les EM sont bien connues dans l'Apprentissage Automatique. Elles génèrent de nombreux classifieurs à partir des données, puis agrègent ces classifieurs en un seul. Le classifieur global obtenu est reconnu pour être très eficace, ce qui a été montré dans de nombreuses études. Cela est possible car il s'appuie sur des informations obtenues auprès de chaque classifieur qui le compose. Nous avons comparé RNC à d'autres méthodes de classification supervisées connues sur des données issues du référentiel UCI Irvine. Nous constatons que RNC fonctionne bien par rapport aux meilleurs d'entre elles, telles que les Forêts Aléatoires (RF) et Support Vector Machines (SVM). La plupart du temps, RNC se classe parmi les trois premières méthodes en terme d'eficacité. Ce résultat nous a encouragé à étudier RNC sur des données réelles comme les tweets. Twitter est un réseau social de micro-blogging. Il est particulièrement utile pour étudier l'opinion à propos de l'actualité et sur tout sujet, en particulier la politique. Cependant, l'extraction de l'opinion politique depuis Twitter pose des défis particuliers. En effet, la taille des messages, le niveau de langage utilisé et ambiguïté des messages rend très diffcile d'utiliser les outils classiques d'analyse de texte basés sur des calculs de fréquence de mots ou des analyses en profondeur de phrases. C'est cela qui a motivé cette étude. Nous proposons d'étudier les couples auteur/sujet pour classer le tweet en fonction de l'opinion de son auteur à propos d'un politicien (un sujet du tweet). Nous proposons une procédure qui porte sur l'identification de ces opinions. Nous pensons que les tweets expriment rarement une opinion objective sur telle ou telle action d'un homme politique mais plus souvent une conviction profonde de son auteur à propos d'un mouvement politique. Détecter l'opinion de quelques auteurs nous permet ensuite d'utiliser la similitude dans les termes employés par les autres pour retrouver ces convictions à plus grande échelle. Cette procédure à 2 étapes, tout d'abord identifier l'opinion de quelques couples de manière semi-automatique afin de constituer un référentiel, puis ensuite d'utiliser l'ensemble des tweets d'un couple (tous les tweets d'un auteur mentionnant un politicien) pour les comparer avec ceux du référentiel. L'Apprentissage Topologique semble être un domaine très intéressant à étudier, en particulier pour résoudre les problèmes de classificationSupervised Learning is a popular field of Machine Learning that has made recent progress. In particular, many methods and procedures have been developed to solve the classification problem. Most classical methods in Supervised Learning use the density estimation of data to construct their classifiers.In this dissertation, we show that the topology of data can be a good alternative in constructing classifiers. We propose using topological graphs like Gabriel graphs (GG) and Relative Neighborhood Graphs (RNG) that can build the topology of data based on its neighborhood structure. To apply this concept, we create a new method called Random Neighborhood Classification (RNC).In this method, we use topological graphs to construct classifiers and then apply Ensemble Methods (EM) to get all relevant information from the data. EM is well known in Machine Learning, generates many classifiers from data and then aggregates these classifiers into one. Aggregate classifiers have been shown to be very efficient in many studies, because it leverages relevant and effective information from each generated classifier. We first compare RNC to other known classification methods using data from the UCI Irvine repository. We find that RNC works very well compared to very efficient methods such as Random Forests and Support Vector Machines. Most of the time, it ranks in the top three methods in efficiency. This result has encouraged us to study the efficiency of RNC on real data like tweets. Twitter, a microblogging Social Network, is especially useful to mine opinion on current affairs and topics that span the range of human interest, including politics. Mining political opinion from Twitter poses peculiar challenges such as the versatility of the authors when they express their political view, that motivate this study. We define a new attribute, called couple, that will be very helpful in the process to study the tweets opinion. A couple is an author that talk about a politician. We propose a new procedure that focuses on identifying the opinion on tweet using couples. We think that focusing on the couples's opinion expressed by several tweets can overcome the problems of analysing each single tweet. This approach can be useful to avoid the versatility, language ambiguity and many other artifacts that are easy to understand for a human being but not automatically for a machine.We use classical Machine Learning techniques like KNN, Random Forests (RF) and also our method RNC. We proceed in two steps : First, we build a reference set of classified couples using Naive Bayes. We also apply a second alternative method to Naive method, sampling plan procedure, to compare and evaluate the results of Naive method. Second, we evaluate the performance of this approach using proximity measures in order to use RNC, RF and KNN. The expirements used are based on real data of tweets from the French presidential election in 2012. The results show that this approach works well and that RNC performs very good in order to classify opinion in tweets.Topological Learning seems to be very intersting field to study, in particular to address the classification problem. Many concepts to get informations from topological graphs need to analyse like the ones described by Aupetit, M. in his work (2005). Our work show that Topological Learning can be an effective way to perform classification problem
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Gulak, Denis. "The impact of information services and social networks on learning English." Thesis, Молодь у глобалізованому світі: академічні аспекти англомовних фахових досліджень (англ. мовою) / Укл., ред. А.І.Раду: збірник мат. конф. - Львів: ПП "Марусич", 2011. - 147 с, 2011. http://er.nau.edu.ua/handle/NAU/20771.
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Pan, Zhengzheng. "Learning, Game Play, and Convergence of Behavior in Evolving Social Networks." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/27460.
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I study information dissemination and opinion formation in a framework of evolving social networks. Individuals take weighted averages repeatedly to update their opinions. They also update their assessments on others' opinions, represented by an influence weight matrix. It is proven that both opinions and the influence weights are convergent. In the steady state, consensus is reached where all individuals hold the same opinion. Convergence occurs with an extended model as well, which indicates the tremendous influential power possessed by a minority group. Then I impose a dual network structure, where individuals not only collect information, but also use the information to play a coordination game with a selected group of opponents that one is connected with. All individuals update their strategies based on a naive learning process within a separate influence network in which information is disseminated. The selection of opponents also gets updated over time. I calculate the critical values of costs associated with connections for different network structures and strategies to occur in the steady state. Finally, I investigate the outcomes of social learning under various exogenous network structures. Individuals use an algorithm that takes into account both proximity of opinions and impact of neighbors. Results also show consensus, with convergence speed correlated with the network structure. In addition, an endogenous network formation in two stages that utilizes network and distance between agents' opinions is proposed. The resulting networks show power-law patterns in degree distribution.Ph. D.
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Idani, Arman. "Assessment of individual differences in online social networks using machine learning." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270109.
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The services that define our personal and professional lives are increasingly accessed through digital devices, which store extensive records of our behaviour. An individual's psychological profile can be accurately assessed using offline behaviour, and I investigate if an automated machine learning system can measure the same psychological factors, only from observing the footprints of online behaviour, without observing any offline behaviour or any direct input from the individual. Prior research shows that psychological traits such as personality can be predicted using these digital footprints, although current state-of-the-art accuracy is below psychometric standards of reliability and self-reports consistently outperform machine-ratings in external validity. I introduce a new machine learning system that is capable of doing five-factor personality assessments, as well as other psychological assessments, from online data as accurately as self-report questionnaires in terms of reliability, internal consistency and external and discriminant validity, and demonstrate that passive psychological assessment can be a realistic option in addition to self-report questionnaires for both research and practice. Achieving this goal is not possible using conventional dimensionality reduction and linear regression models. Here I develop a supervised dimensionality reduction method capable of intelligently selecting only useful parts of data for the relevant prediction at hand which also does not lose variance when eliminating redundancies. In the learning stage, instead of linear regression models, I use an ensemble of decision trees which are able to distinguish scenarios where the same observations on digital data can mean different things for different individuals. This work highlights the interesting idea that similar to how a human expert who is able to assess personality from offline behaviour, an expert machine learning system is able to assess personality from online behaviour. It also demonstrates that big-5 personality are predictors of how predictable users are in social media, with neuroticism having the greatest correlation with unpredictability, while openness having the greatest correlation with predictability.
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Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.
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Rawashdeh, Ahmad. "Semantic Similarity of Node Profiles in Social Networks." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439279922.
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Magnusson, Jonathan. "Social Network Analysis Utilizing Big Data Technology." Thesis, Uppsala universitet, Avdelningen för datalogi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170926.
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As of late there has been an immense increase of data within modern society. This is evident within the field of telecommunications. The amount of mobile data is growing fast. For a telecommunication operator, this provides means of getting more information of specific subscribers. The applications of this are many, such as segmentation for marketing purposes or detection of churners, people about to switching operator. Thus the analysis and information extraction is of great value. An approach of this analysis is that of social network analysis. Utilizing such methods yields ways of finding the importance of each individual subscriber in the network. This thesis aims at investigating the usefulness of social network analysis in telecommunication networks. As these networks can be very large the methods used to study them must scale linearly when the network size increases. Thus, an integral part of the study is to determine which social network analysis algorithms that have this scalability. Moreover, comparisons of software solutions are performed to find product suitable for these specific tasks. Another important part of using social network analysis is to be able to interpret the results. This can be cumbersome without expert knowledge. For that reason, a complete process flow for finding influential subscribers in a telecommunication network has been developed. The flow uses input easily available to the telecommunication operator. In addition to using social network analysis, machine learning is employed to uncover what behavior is associated with influence and pinpointing subscribers behaving accordingly.
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Wang, Xi. "Learning Collective Behavior in Multi-relational Networks." Doctoral diss., University of Central Florida, 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6379.
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With the rapid expansion of the Internet and WWW, the problem of analyzing social media data has received an increasing amount of attention in the past decade. The boom in social media platforms offers many possibilities to study human collective behavior and interactions on an unprecedented scale. In the past, much work has been done on the problem of learning from networked data with homogeneous topologies, where instances are explicitly or implicitly inter-connected by a single type of relationship. In contrast to traditional content-only classification methods, relational learning succeeds in improving classification performance by leveraging the correlation of the labels between linked instances. However, networked data extracted from social media, web pages, and bibliographic databases can contain entities of multiple classes and linked by various causal reasons, hence treating all links in a homogeneous way can limit the performance of relational classifiers. Learning the collective behavior and interactions in heterogeneous networks becomes much more complex.
The contribution of this dissertation include 1) two classification frameworks for identifying human collective behavior in multi-relational social networks; 2) unsupervised and supervised learning models for relationship prediction in multi-relational collaborative networks. Our methods improve the performance of homogeneous predictive models by differentiating heterogeneous relations and capturing the prominent interaction patterns underlying the network structure. The work has been evaluated in various real-world social networks. We believe that this study will be useful for analyzing human collective behavior and interactions specifically in the scenario when the heterogeneous relationships in the network arise from various causal reasons.Ph.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering
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Wang, Hao, and Yun Xie. "The use of Online Social Networks in Chinese Collaborative E-learning Education." Thesis, Örebro universitet, Handelshögskolan vid Örebro universitet, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-16037.
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Wu, Pi-Chu. "Social networks, language learning and language school student sojourners : a qualitative study." Thesis, University of Warwick, 2008. http://wrap.warwick.ac.uk/2132/.
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This thesis investigates individual language school student sojourners’ learning experiences in the UK. It takes into account the importance of learners learning through interaction with others in the target language community and recognises how learners’ social networks affect their language learning and use. There are many studies about students who study abroad for academic purposes and immigrant learners, but not many relating to individual language learners in private language schools. In order to fill this gap, this study focused on individual language school learners. With the intention of understanding how the social networks and language learning interact over time as part of their sojourner experience, I utilised different theoretical frameworks that have been applied to other groups of language learners and concluded that these frameworks are also applicable to private language school students. I recognised that in order to understand my participants’ learning experiences I needed to interact with them and also observe how they interacted with their social world. Therefore, formal interviews (semi-structured) and informal interviews (informal group meeting or chat) were the main methods for my study together with observation of interaction in various situations. This study identified the expectations that learners had with regard to the target language community, host families and native speakers’ attitudes towards foreign students, and the realisation that these expectations were higher than what was actually encountered. It also identified the steps the individual language learners took to overcome these disappointments and how they reconstructed their relationships with the target language and community respectively. In contrast with many previous studies which only focused on learning from native speakers, my study recognised that learners sometimes can have more interpersonal contact with their fellow students than native speakers. And consequently they benefit more from these contacts, in terms of language learning, than from native speakers. This thesis also helps language learners and language educators recognise basic theoretical frameworks which could help them evaluate the benefits and problems related to learning through interpersonal contact. And with this understanding learners will be able to facilitate their autonomous learning in the target language community.
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Salehi, Rizi Fatemeh [Verfasser], and Michael [Akademischer Betreuer] Granitzer. "Graph Representation Learning for Social Networks / Fatemeh Salehi Rizi ; Betreuer: Michael Granitzer." Passau : Universität Passau, 2021. http://d-nb.info/1238343821/34.
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Jayarathna, Lakmali. "Effective use of social media networks for collaborative learning in higher education." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235152/13/Lakmali_Jayarathna_Thesis.pdf.
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With the continual rise of online and hybrid modes of learning, students collaborating through social media networks (SMN) is becoming increasingly important. Yet, students are struggling to unleash the potential of SMN to support their learning. This study investigated how SMN can be effectively used for collaborative learning in higher education through using a mixed methodology. Findings showed that SMN's effective use influenced students’ collaborative learning goal attainment and was facilitated by perception of SMN affordances and social capital. This thesis extends the theory of effective use by proposing an approach for contextualizing effective use of SMN for collaborative learning.
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Campana, Giuseppe. "Professional development and social networks in digital content industry micro businesses." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/71764/1/Giuseppe_Campana_Thesis.pdf.
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This study explores the professional development strategies of digital content professionals in Australian micro businesses. This thesis presents the argument that as these professionals are working in cutting edge creative fields where digital technology drives ongoing change, formal education experiences may be less important than for other professionals, and that specific types of online and face-to-face socially mediated informal learning strategies may be critical to currency. This thesis documents the findings of a broad survey of industry professionals' learning needs and development strategies, in conjunction with rich data from in-depth interviews and social network analyses.
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Lippold, Tessa. "The significance of social support and close relationships for people with learning disabilities." Thesis, n.p, 2000. http://ethos.bl.uk/.
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Rapanos, Theodoros. "Essays on the Economics of Networks Under Incomplete Information." Doctoral thesis, Stockholms universitet, Nationalekonomiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-132199.
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Social networks constitute a major channel for the diffusion of information and the formation of attitudes in a society. Introducing a dynamic model of social learning, the first part of this thesis studies the emergence of socially influential individuals and groups, and identifies the characteristics that make them influential. The second part uses a Bayesian network game to analyse the role of social interaction and conformism in the making of decisions whose returns or costs are ex ante uncertain.
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Yang, Guoli. "Learning in adaptive networks : analytical and computational approaches." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20956.
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The dynamics on networks and the dynamics of networks are usually entangled with each other in many highly connected systems, where the former means the evolution of state and the latter means the adaptation of structure. In this thesis, we will study the coupled dynamics through analytical and computational approaches, where the adaptive networks are driven by learning of various complexities. Firstly, we investigate information diffusion on networks through an adaptive voter model, where two opinions are competing for the dominance. Two types of dynamics facilitate the agreement between neighbours: one is pairwise imitation and the other is link rewiring. As the rewiring strength increases, the network of voters will transform from consensus to fragmentation. By exploring various strategies for structure adaptation and state evolution, our results suggest that network configuration is highly influenced by range-based rewiring and biased imitation. In particular, some approximation techniques are proposed to capture the dynamics analytically through moment-closure differential equations. Secondly, we study an evolutionary model under the framework of natural selection. In a structured community made up of cooperators and cheaters (or defectors), a new-born player will adopt a strategy and reorganise its neighbourhood based on social inheritance. Starting from a cooperative population, an invading cheater may spread in the population occasionally leading to the collapse of cooperation. Such a collapse unfolds rapidly with the change of external conditions, bearing the traits of a critical transition. In order to detect the risk of invasions, some indicators based on population composition and network structure are proposed to signal the fragility of communities. Through the analyses of consistency and accuracy, our results suggest possible avenues for detecting the loss of cooperation in evolving networks. Lastly, we incorporate distributed learning into adaptive agents coordination, which emerges as a consequence of rational individual behaviours. A generic framework of work-learn-adapt (WLA) is proposed to foster the success of agents organisation. To gain higher organisation performance, the division of labour is achieved by a series of events of state evolution and structure adaptation. Importantly, agents are able to adjust their states and structures through quantitative information obtained from distributed learning. The adaptive networks driven by explicit learning pave the way for a better understanding of intelligent organisations in real world.
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Shekfeh, Marwa. "MANILA: A Multi-Agent Framework for Emergent Associative Learning and Creativity in Social Networks." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511861383686974.
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Al-Janabi, Mohammed Fadhil Zamil. "Detection of suspicious URLs in online social networks using supervised machine learning algorithms." Thesis, Keele University, 2018. http://eprints.keele.ac.uk/5581/.
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This thesis proposes the use of several supervised machine learning classification models that were built to detect the distribution of malicious content in OSNs. The main focus was on ensemble learning algorithms such as Random Forest, gradient boosting trees, extra trees, and XGBoost. Features were used to identify social network posts that contain malicious URLs derived from several sources, such as domain WHOIS record, web page content, URL lexical and redirection data, and Twitter metadata. The thesis describes a systematic analysis of the hyper-parameters of tree-based models. The impact of key parameters, such as the number of trees, depth of trees and minimum size of leaf nodes on classification performance, was assessed. The results show that controlling the complexity of Random Forest classifiers applied to social media spam is essential to avoid overfitting and optimise performance. The model complexity could be reduced by removing uninformative features, as the complexity they add to the model is greater than the advantages they give to the model to make decisions. Moreover, model-combining methods were tested, which are the voting and stacking methods. Both show advantages and disadvantages; however, in general, they appear to provide a statistically significant improvement in comparison to the highest singular model. The critical benefit of applying the stacking method to automate the model selection process is that it is effective in giving more weight to more topperforming models and less affected by weak ones. Finally, 'SuspectRate', an online malicious URL detection system, was built to offer a service to give a suspicious probability of tweets with attached URLs. A key feature of this system is that it can dynamically retrain and expand current models.
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Eberlen, Julia. "Learning about Groups: The Self and Social Networks in the Emergence of Stereotypes." Doctoral thesis, Universite Libre de Bruxelles, 2020. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/305347.
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Stereotypes are often considered to exist as a consequence of categorizing people into different groups, our belonging (or not) to the group, and our exposure to perceived covariation between group member’s physical and psychological characteristics However, the process of stereotype learning itself is not always taken into account. In my thesis, I hypothesized that people will categorize artificial social stimuli into groups, and learn to associate them with descriptions compatible with stereotype content, even if they are not actively made aware of such a connection. This learning process is influenced by the individuals process on the one, and its social environment on the other. First, we find that people learn stereotypes in the absence of explicit information about the existence of social outgroups. For stimuli with equal baseline valence, the contrast between perceived stereotypes was stronger when the stereotypical information was more distinctive for one social group over the other. When baseline valence was not the same, stereotype-consistent information increased this difference while non-stereotypical information led to less stereotypical differentiation between the groups.Second, when participants are themselves part of a group, stereotype consistent information is readily integrated into the group impression. For stereotype inconsistent information, and within the context of experimentally induced group belonging, participants did learn the (counter) stereotypical information, but the identification with the group decreased. Finally, the focus is again on outgroup stereotype learning, but this time in the context of small social networks. The aim here was to isolate network structure from social interaction per se and investigate whether structure alone influences the emergence of stereotypes. People learned from each other in these distinct network configurations, i.e. in a fully connected network or a star network. Different from the other studies, participants decided themselves how a member of our experimental social groups was presented to other participants. This way, we could observe whether stereotypes emerge as a consequence of social interaction, restricted by their network ties. We found that participants will learn outgroup stereotypes consistent with the covariation between group stimuli and description to which they were exposed in their respective network. However, the network structure itself does not contribute to stereotype learning or emergence.Doctorat en Sciences psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished
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Hoyle, Sally G. "Stability and change in social relations of children with and without learning disabilities : social status, social networks, perceived social competence, social cognition, behavior problems, and ecological factors /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487322984315161.
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Pérez-Solà, Cristina. "Towards understanding privacy risks in online social networks." Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/386415.
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Les xarxes socials en línia (en anglès, Online Social Networks o OSNs) són avui en dia un dels serveis més populars a Internet. En el moment d’escriure aquestes línies, quatre de les deu primeres pàgines del rànquing global Alexa corresponien a xarxes socials i les xarxes més utilitzades tenien centenars de milions d’usuaris actius cada dia.
Les persones fem servir xarxes socials per compartir tot tipus de continguts: des d’atributs personals (com noms, edat o sexe), a ubicacions, fotos o comentaris. D’altra banda, les xarxes socials es caracteritzen per permetre que els usuaris puguin crear relacions de manera explícita (per exemple, relacions d’amistat). A més, les xarxes socials inclouen no només la informació que els usuaris publiquen conscientment sobre si mateixos, sinó també la informació que es genera a partir de la interacció dels usuaris de la plataforma.
Tant el nombre d’usuaris com el volum de dades compartides fan que la privacitat en xarxes socials sigui crítica. Aquesta tesi se centra en l’estudi de la privacitat en xarxes socials en dos contextos diferents: l’adquisició de dades de manera automatitzada (crawling) i l’aprenentatge. En primer lloc, s’estudia la relació entre crawling i privacitat, un tema que fins al moment ha rebut una atenció limitada. Aquest escenari és interessant ja que és assequible fins i tot per a un atacant de baix pressupost. En segon lloc, s’estudia com extreure informació de les relacions que formen els usuaris de xarxes socials. Les tècniques desenvolupades s’estenen després al tractament d’altres problemes que, com les xarxes socials, es poden modelar en forma de grafs.Online Social Networks (OSNs) are now one of the most popular services on the Internet. When these lines were written, there were four OSN sites in the Alexa's top ten global ranking and the most used OSNs were having hundreds of millions of daily active users. People use OSNs to share all kinds of contents: from personal attributes (like names, age, or gender), to location data, photos, or comments. Moreover, OSNs are characterized by allowing its users to explictly form relationships (e.g. friendship). Additionally, OSNs include not only information the users conscientiously post about themselves, but also information that is generated from the interaction of users in the platform. Both the number of users and the volume of data shared make privacy in OSNs critical. This thesis is focused on studying privacy related to OSNs in two different contexts: crawling and learning. First, we study the relation between OSN crawling and privacy, a topic that so far received limited attention. We find this scenario interesting because it is affordable for even a low-budget attacker. Second, we study how to extract information from the relationships OSN users form. We then expand our findings to other graph-modeled problems.
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Ashton, Philippa. "The social context for design learning : an investigation of social networks in the undergraduate design school studio." Thesis, Staffordshire University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.247291.
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Wilkie, Tara V. "A qualitative investigation into adolescents with learning disabilities : their perceptions and uses of social support." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0021/NQ55392.pdf.
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Abufouda, Mohammed [Verfasser], and Katharina [Akademischer Betreuer] Zweig. "Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics / Mohammed Abufouda ; Betreuer: Katharina Zweig." Kaiserslautern : Technische Universität Kaiserslautern, 2020. http://d-nb.info/1221599747/34.
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Eller, Linda S. "Social media as avenue for personal learning for educators: Personal learning networks encourage application of knowledge and skills." PEPPERDINE UNIVERSITY, 2012. http://pqdtopen.proquest.com/#viewpdf?dispub=3498101.
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Malerba, Candilio Maria Luisa. "Social Networking in Second Language Learning." Doctoral thesis, Universitat Oberta de Catalunya, 2015. http://hdl.handle.net/10803/565551.
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Aquesta tesi se centra en l'aprenentatge informal d'una segona llengua en comunitats en línia com Livemocha i Busuu. Els objectius són: (1) analitzar el potencial de les comunitats en línia per a aconseguir resultats d'aprenentatge a llarg termini; (2) examinar les accions dels estudiants mentre construeixen oportunitats d'ús de la segona llengua en aquests entorns, i (3) explorar les potencialitats i les limitacions de les eines de les comunitats en línia. Amb la finalitat d'assolir aquests objectius, l'estudi, que s'inscriu en el marc teòric de la perspectiva sociocultural i de la teoria de l'activitat, ha utilitzat una metodologia de recerca principalment qualitativa i centrada en el mètode etnogràfic. La recerca conclou amb una reflexió crítica sobre la importància de l'autonomia de l'estudiant. S'ha destacat que l'autonomia de l'estudiant és un requisit important perquè l'experiència d'aprenentatge informal en aquests entorns sigui eficaç. A més, aquest estudi tradueix els resultats obtinguts en un conjunt de recomanacions pedagògiques dirigides a experts d'entorns d'aprenentatge, a estudiants i a professors d'idiomes, per tal de fomentar una experiència d'aprenentatge en les comunitats en línia més positiva tenint en compte, també, la seva possible aplicació en un context d'aprenentatge formal.Esta tesis está centrada en el aprendizaje informal de una segunda lengua en comunidades en línea como Livemocha y Busuu. Los objetivos son: (1) analizar el potencial de las comunidades en línea para lograr resultados de aprendizaje a largo plazo; (2) examinar las acciones de los estudiantes mientras construyen oportunidades de uso de la segunda lengua en estos entornos, y (3) explorar las potencialidades y las limitaciones de las herramientas de las comunidades en línea. Con la finalidad de alcanzar estos objetivos, el estudio, que se inscribe en el marco teórico de la perspectiva sociocultural y de la teoría de la actividad, ha utilizado una metodología de investigación principalmente cualitativa y centrada en el método etnográfico. La investigación concluye con una reflexión crítica sobre la importancia de la autonomía del estudiante. Se ha destacado que la autonomía del estudiante es un requisito importante para que la experiencia de aprendizaje informal en estos entornos sea eficaz. Además, este estudio traduce los resultados obtenidos en una serie de recomendaciones pedagógicas dirigidas a expertos de entornos de aprendizaje, a estudiantes y a profesores de idiomas, con el fin de fomentar una mejor experiencia de aprendizaje en las comunidades en línea tomando en consideración también su posible aplicación en un contexto de aprendizaje formal.
This thesis deals with informal second language learning in online communities such as Livemocha and Busuu. The thesis' objectives are: (1) analyse the potential effectiveness of these communities for long-term learning outcomes; (2) examine learners' construction of opportunities for L2 use in these environments; (3) explore affordances and constraints of online communities. To this end, a longitudinal multiple ethnographic case study approach was used under the theoretical framework of Socio-Cultural Theory and Activity Theory (AT). The research concludes with a critical reflection on the role of learner autonomy as a prerequisite for the creation of effective learning experiences in these environments, as this study clearly demonstrates. Moreover, the study translates its findings into a set of pedagogical recommendations for platform developers, learners and teachers to maximize the advantages of L2 learning in online communities as well as establish possible applications in formal learning settings.
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Åsberg, Samira. "Social Networks in Education: A Facebook-Based Educational Platform." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93649.
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Social networking sites are among the most popular daily activities of students these days. Students are mostly using social networking sites for communication and sharing of their experiences. Facebook is an example of a social networking site, which supports additional features such as creating a profile page, creating group pages and supports possibility of implementing different integrated application with Facebook. These features improve the Facebook experience, allowing users to form groups, where they can introduce ideas and concepts, which can be shared and discussed in a structured style. For this thesis we have created a new learning management system by implementing an online educational platform within a Facebook context. This work introduces a new, complementary style of education, where students can improve their knowledge and sociality outside the university in an innovative way. The platform takes advantage of gamification, which introduces game-like elements to concepts such as education and learning management systems, to make them more fun and rewarding. The goal of this thesis is to extend the educational border to an interesting online environment where students can learn, communicate, and examine their knowledge globally in different courses within our application platform in Facebook.
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Papadimitriou, Aristea. "The Future of Communication: Artificial Intelligence and Social Networks." Thesis, Malmö högskola, Fakulteten för kultur och samhälle (KS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-21886.
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The rapid evolution of technology and social media has brought significant changes to human communication. Since the efficiency of social networks depends mainly on the processing of their huge amount of collected data, they are all in search not only of the latest artificial intelligence but also of the creation of more evolved one. Advertising, digital marketing and customer service of social media is in the first line for this demand, yet the rapid progress in the AI field constantly change the ways of communication and the ramifications of this change are more than modern society can absorb and reflect on. This paper focuses on the latest innovations of AI in the social networks and the impact of AI on society and personhood.
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Olamijulo, Christianah. "An investigation into integrating social sites as a teaching and learning practice to create dialogue spaces in the language classroom." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1020149.
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This study intends to explore how social media or social networking sites (SNSs) such as Facebook can facilitate communication channels or create dialogue spaces in a language class. Social media is a form of participatory media, which broadly refers to the “collection of communication channels or mediums (primarily online and mobile) through which social networks originate and are sustained” (Flew 2008:109). Although the term social media is often used as a collective term for SNSs or as the core trademark of Web 2.0, Flew (2008:17) also distinguishes social media by calling it a “communications infrastructure” that allows for “participation, interactivity, collaborative learning and social networking”. Flew (2008) identifies various online sites including the online encyclopaedia Wikipedia and the online user-generated video site YouTube as well as various personalised web space sites such as MySpace, Facebook, Friendster and Bebo as participatory media. The study’s data collection was situated at Nelson Mandela Metropolitan University (NMMU) and investigated how social media can be used to facilitate dialogue between a tutor and BKI1120 Communication in English B students in a Higher Education (HE) context using qualitative methodology. This study compared the use of existing and more traditional or conventional classroom communication practices with those of SNSs as a communication channel, while focusing on social media application as a communication tool to create dialogue spaces that support teaching and learning practices. The research also attempted to identify alternative applications of social media for teaching and learning practices to inform researchers in the fields of HE and media. In the first data-collection phase, BKI1120 Communication in English B Public Management students were selected as the sample for the study. Seventeen students participated in the BKI1120 Facebook page created for the purpose of this study. In the second data-collection phase, a taped focus-group interview was conducted with eight BKI1120 Communication in English B students. The interview transcript was then analysed qualitatively for themes. The research findings showed that social media or SNSs such as Facebook can facilitate communication channels or create dialogue spaces in a language class, if it is managed effectively.
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Lögdlund, Ulrik. "Networks and Nodes : The Practices of Local Learning Centres." Doctoral thesis, Linköpings universitet, Avdelningen för sociologi (SOC), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-69601.
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This thesis focuses on the practice of local learning centres in Sweden. The aim is to describe and to establish an understanding of relations and the actor-networks that surround the practice. The thesis is based on four different studies. The first two studies focus on the organisation of the local learning centres and scrutinises the relation between the local learning centres, municipal administration and business in the region. What types of relations exist and what strategies are used by the local learning centres to enrol actors into the actor-network? The two latter studies investigate videoconference in the context of local learning centres. Videoconference is described as a socio-technical environment. The studies focus on interaction and communications asking what kind of relations are created between environment, material design and people? The theoretical framework consists of actor-network theory and the notions of space and spatial relations. The methods used in the four studies are interviews and observations. Informants that have contributed to the study are headmasters, teachers and students as well as project managers, politicians and businessmen. The results of the studies show how the local learning centres fails as brokers on an educational market. The reasons are many. The main results of the studies stress a number of obstacles of involvement and shed light on different strategies of enrolment. The results connected to videoconference show how material design and technology impacts the practice. Different strategies are developed by participants to manoeuvre in the videoconferencing classrooms. In conclusion the four studies show how actor-networks influence the practice of local learning centres by representations.Denna avhandling är en studie om lärcentra i Sverige. Syftet är att beskriva och öka kunskapen om de relationer och de aktörsnätverk som omger praktiken. Avhandlingen bygger på resultat från fyra olika delstudier. Fokus i två av dessa ligger på lärcentra som organisation. Hur ser relationen mellan lärcentra och omgivande aktörer ut i regionen och vilka strategier används för att skapa aktörsnätverk? De övriga två studierna handlar om videokonferens där fokus ligger på hur relationer skapas mellan miljö, teknik och människor. Särskilt studeras interaktion och kommunikation mellan dessa aktörer i en utpräglat socioteknisk lärandemiljö. Den teoretiska ramen för de olika delstudierna är aktörsnätverksteori som används tillsammans med begrepp som spatiala relationer. De fyra studierna använder sig i huvudsak av kvalitativa metoder som intervjuer och observationsstudier. Datainsamlingen berör en bred samling informanter som rektorer, lärare och studenter tillsammans med projektledare, politiker och företagare. Studiernas resultat visar att det finns skilda synsätt på utbildning och kompetens mellan olika grupper av aktörer. Trots involveringsstrategier av aktörer från omgivande aktörsnätverk lyckas man inte agera som en mäklare på en utbildningsmarknad. Resultaten visar vidare att miljö tillsammans med teknik har stort inflytande på hur studenter och lärare agerar i videokonferensklassrummen. Det är den materiella designen och den tekniska logiken som styr praktiken. Resultaten visar också på hur olika studerandestrategier utvecklas för att stå utanför interaktion i klassrummet tillsammans med hur lärares kommunikation utvecklas för att överbrygga avståndet till de studerande. Sammantaget visar de fyra studierna på hur olika aktörsnätverk inverkar på praktiken genom representationer.
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Suter, Deitra L. "The Role of Religion in Predicting Recidivism: Considering Elements of Social Networking, Social Capital, and Social Learning Theories." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=bgsu1131134485.
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Fang, Chunsheng. "Novel Frameworks for Mining Heterogeneous and Dynamic Networks." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978.
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Wu, Hao, and 吴颢. "SNS use in teaching and learning in China." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/198865.
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Social Network Sites (SNSs) are increasingly influencing the academic and industry researchers intrigued by their affordances and settings. Although SNS has been claimed that it occupied too much of peoples’ daily lives by many researchers and scientists, the fact also demonstrates the growing use of SNS in education area, which proved that SNS has its unique pedagogical significance and potential to foster students’ learning experience. Used and promoted in many countries around the world, SNS is now not only serving as a social network environment (SNE) for people to communicate, but also a platform for academic information exchange and sharing in various contexts. This study aimed at exploring the differences between western and Chinese localised SNS, evaluating the practicability of SNS use in China’s education, and identifying certain problems in the implementation. Referenced by the literature on SNS experiment in teaching, cultural influence and pedagogy value, Chinese SNS would be analysed from comprehensive perspectives.
This study combined two parts, one was the comparison study for four selected SNSs from China and foreign countries, the other was the research experiment conducted with a class of 17 students enrolling in an English educational institution in Chinese mainland, where the students took a spoken English course which lasted for one and a half month. The selected SNSs used in the experiment were implemented to facilitate the teaching and support designed learning process that requires students to explore the use of SNS and motivate them to interact more with peers and teacher off-class while completing the course objectives.
Date collection and analysis conducted mixed methods in this research, the data of this research were collected by different levels of participants’ interviews, observations, recordings and questionnaires that covers the perceptions for research topic, everyday use of SNSs, taken-forgranted interactions and communication among teacher and students in the learning and teaching process.
This research not only justify the practicability of SNS use in China’s education, it also revealed various functions that SNSs could provide in China’s education. In terms of functionality, it could serves as a platform for (a) facilitating presentation and demonstration, (b) developing personal learning record and portfolios, (c) distributing and sharing resources, (d) promoting student-teacher offline interaction, (e) enabling free comment and feedbacks.
Theoretical research would be conducted and practical implementation would also be introduced. Through the experiment, the research would make the best of SNSs in education, which further discuss the special characteristics of Chinese localised SNSs and broaden the understanding of using SNS in education. Pedagogical principles are also discussed.published_or_final_version
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Master of Education