Submitted by Biblioteca de Pós-Graduação em Geoquímica BGQ (bgq@ndc.uff.br) on 2017-12-18T15:08:18Z No. of bitstreams: 1 Dissertação_Verônica de Souza Gomes_UNIRIO_2017.pdf: 2390659 bytes, checksum: 1d9ff7f83da9d70b3a822a7bacc7743b (MD5) / Made available in DSpace on 2017-12-18T15:08:18Z (GMT). No. of bitstreams: 1 Dissertação_Verônica de Souza Gomes_UNIRIO_2017.pdf: 2390659 bytes, checksum: 1d9ff7f83da9d70b3a822a7bacc7743b (MD5) / Universidade Federal Fluminense. Instituto de Química. Programa de Pós-Graduação em Geoquímica, Niterói, RJ / Esta pesquisa tem por objetivo investigar a estrutura científica do Programa de Pós-Graduação em Geociências (Geoquímica) da Universidade Federal Fluminense, tendo como referência os atores científicos, a fim contextualizar e verificar o desempenho do PPG-Geo/UFF, e contribuir com o alinhamento às diretrizes do SNPG. Tem-se como premissa que a complexidade da pesquisa científica envolve uma organização social dos atores na forma de rede. Neste sentido, a pesquisa é um estudo de caso, que parte de uma fase exploratória, que adota como metodologia uma pesquisa documental, Análise de Reses Sociais (ARS) e aplicação de questionário. O resultado contribui sistematicamente para identificar os atores que sustentam o campo científico da Geoquímica na UFF e suas comunidades epistêmicas, o conhecimento das práticas de produção e compartilhamento do conhecimento e de informações considerando a Política de Pós-Graduação no Brasil. Por fim, a contribuição da pesquisa para o campo da Geoquímica está representada na construção de diretrizes, entre elas a identificação de projetos já existentes a fim de atender a demanda do SNPG em relação à Educação Básica; ampliação da visibilidade do Programa por meio da implantação da plataforma ‘SOMOS UFF’; complementar a inserção de itens importantes para ampliação do conhecimento e da visibilidade no site do Programa; e o fortalecimento dos elos com a biblioteca, em busca de uma relação maior entre pesquisador e bibliotecário. Conclui-se que através das pesquisas, atividades desenvolvidas, relações estabelecidas, entre outros meios, o PPG-Geo/UFF na sua área de atuação vem contribuindo fortemente para o progresso da Ciência. / This research aims to investigate the scientific structure of the Graduate Program in Geosciences (Geochemistry) of Universidade Federal Fluminense, with reference to the scientific actors, in order to contextualize and verify the performance of PPG-Geo/UFF, and contribute to the alignment with the SNPG guidelines. The premise is that the complexity of scientific research involves a social organization of the actors in the form of network. In this sense, the research is a case study, which starts from an exploratory phase, which adopts as methodology a documentary research, Social Network Analysis (SNA) and questionnaire application. The result contributes systematically to identify the actors who sustain the scientific field of Geochemistry at UFF and their epistemic communities, knowledge production practices and knowledge sharing and information considering postgraduate policy in Brazil. Finally, the contribution of research to the field of Geochemistry is represented in building guidelines, including the identification of existing projects in order to meet the demand of SNPG in relation to basic education; increasing the visibility of the program through the implementation of the platform ‘We are UFF’; complementing the insertion of important items for broadening knowledge and visibility on the site of the Program; strengthening links with the library in search of a larger relationship between researchers and librarians. Concluding that through research, activities developed, relations established, among other means, the PPG-Geo/UFF in its area of action has been contributing strongly to the progress of S

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2017-03-01T21:34:06Z No. of bitstreams: 1 Ronan Kayano Genoino.pdf: 2728242 bytes, checksum: de5f6fa2af63f54ea2a868aa133a9a36 (MD5) / Made available in DSpace on 2017-03-01T21:34:06Z (GMT). No. of bitstreams: 1 Ronan Kayano Genoino.pdf: 2728242 bytes, checksum: de5f6fa2af63f54ea2a868aa133a9a36 (MD5) Previous issue date: 2016-12-14 / This study aimed to verify which leads an actor to have greater centrality in the trust networks, that verifies the relationship between organizational commitment, technical ability, and time in the team (independent variables) with the centrality of the athletes in the trust networks of team sports (dependent variable). This research was based on the theory of social networks, one of the topics most discussed in the social sciences in the last decade, specifically the trust networks, a kind of informal network that is formed by interpersonal trust relationships between the members of an organization. The study was a qualitative research with an exploratory descriptive done with athletes of the Centro Olímpico de Treinamento e Pesquisa. Questionnaires were applied in 49 athletes from three different teams, to collect socio-metric data, to measure the trust network (used the UCINET software), organizational commitment, technical ability of players and time in the team. Moreover, semi-structured interviews were conducted among coaches of the three teams. The results indicated that the research question in this project was answered, since the field provided empirical evidence about a positive relationship between organizational commitment, time in the team, technical ability, and the centrality of athletes in trust networks. The main practical application is the parallel that can be done with the managerial field, in which more committed and qualified professionals, with more time in the organizations, can enjoy greater confidence within the corporate environment, resulting in better organizational performance. / O presente trabalho teve como objetivo verificar o que leva um ator a ter maior centralidade nas redes de confiança, verificando a relação entre o comprometimento organizacional, a qualidade técnica e o tempo na equipe (variáveis independentes) com a centralidade dos atletas nas redes de confiança de equipes esportivas (variável dependente). A pesquisa teve como base a teoria das redes sociais, mais especificamente as redes de confiança, um tipo de rede informal que é formada pelas relações de confiança interpessoal entre os membros de uma organização. O estudo foi uma pesquisa qualitativa com caráter exploratório-descritivo, feita com atletas do Centro Olímpico de Treinamento e Pesquisa. Questionários foram aplicados em 49 atletas de três equipes diferentes. Foram coletados dados sócio-métricos, para mensuração da rede de confiança (utilizou-se o software UCINET), comprometimento organizacional, qualidade técnica dos jogadores e tempo na equipe. Além disso, foram realizadas entrevistas semi-estruturadas com os treinadores das três equipes. Os resultados obtidos indicaram que a questão de pesquisa do presente estudo foi respondida, uma vez que o campo forneceu evidências empíricas da existência de uma relação positiva entre o comprometimento organizacional, tempo na equipe e qualidade técnica e a centralidade dos atletas nas redes de confiança. A principal contribuição para a prática é o paralelo que pode ser feito com o campo gerencial, em que profissionais mais comprometidos e qualificados e com mais tempo dentro das organizações podem gozar de maior confiança dentro do ambiente corporativo, resultando em um melhor desempenho organizacional.

This work reports on stable (or invariant) topological properties and textual differentiation in human interaction networks, with benchmarks derived from public email lists. Activity along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free outline, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erdös-Rényi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, 3-12% of the vertices are hubs, 15-45% are intermediary and 44-81% are peripheral vertices. Texts from each of such sectors are shown to be very different through direct measurements and through an adaptation of the Kolmogorov-Smirnov test. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria. For guiding and supporting this research, we also developed a visualization method of dynamic networks through animations. To facilitate verification and further steps in the analyses, we supply a linked data representation of data related to our results. / Este trabalho relata propriedades topológicas estáveis (ou invariantes) e diferenciação textual em redes de interação humana, com referências derivadas de listas públicas de e-mail. A atividade ao longo do tempo e a topologia foram observadas em instantâneos ao longo de uma linha do tempo e em diferentes escalas. A análise mostra que a atividade é praticamente a mesma para todas as redes em escalas temporais de segundos a meses. As componentes principais dos participantes no espaço das métricas topológicas mantêm-se praticamente inalteradas quando diferentes conjuntos de mensagens são considerados. A atividade dos participantes segue o esperado perfil livre de escala, produzindo, assim, as classes de vértices dos hubs, dos intermediários e dos periféricos em comparação com o modelo Erdös-Rényi. Os tamanhos relativos destes três setores são essencialmente os mesmos para todas as listas de e-mail e ao longo do tempo. Normalmente, 3-12% dos vértices são hubs, 15-45% são intermediários e 44-81% são vértices periféricos. Os textos de cada um destes setores são considerados muito diferentes através de uma adaptação dos testes de Kolmogorov-Smirnov. Estas propriedades são consistentes com a literatura e podem ser gerais para redes de interação humana, o que tem implicações importantes para o estabelecimento de uma tipologia dos participantes com base em critérios quantitativos. De modo a guiar e apoiar esta pesquisa, também desenvolvemos um método de visualização para redes dinâmicas através de animações. Para facilitar a verificação e passos seguintes nas análises, fornecemos uma representação em dados ligados dos dados relacionados aos nossos resultados.

Made available in DSpace on 2016-03-15T19:37:29Z (GMT). No. of bitstreams: 1 Evandro Luquini.pdf: 2663730 bytes, checksum: 45c13b956053ea1328342984d7ecf01c (MD5) Previous issue date: 2010-08-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The broad topic of this research is related to computer supported learning and teaching environment. In particular due to the high society´s demand for human resources capable of building and maintaining computer systems the specific topic of this research is focused on computer tools to support programming learning and teaching using techniques from social network and plagiarism detection. Currently the teachers involved with programming teaching have a set of tools to detect plagiarism of source code in the exercises and assessments made by their students. Although these tools are useful as disciplinary measures they do not allow teachers to think about plagiarism from a social perspective. This paper develops and exploits the assumption that plagiarism in the context of programming teaching does not happen in isolation from the social network formed by students in the classroom. From this formulation is suggested that the plagiarism detection algorithms and the students source code presented during the course are sufficient to model indirectly the social network established by the group of students. An exploratory experiment was conducted to evaluate this hypothesis and an intervention procedure was proposed. This procedure was inspired by immunization techniques from social networks and it has the goal to improve learning. / Este trabalho está inserido no contexto das pesquisas que procuram desenvolver ambientes computacionais que apóiam efetivamente os processos de ensino aprendizagem. Em especial, devido à grande demanda da sociedade por recursos humanos capazes de construir e manter sistemas computacionais. O tema específico desta pesquisa concentra-se na aplicação de técnicas de redes sociais e algoritmos de detecção de plágio à questão da aprendizagem de programação. Atualmente os professores envolvidos com o ensino de programação possuem um conjunto de ferramentas capazes de detectar o plágio de código-fonte nos exercícios e avaliações realizados por seus alunos. Apesar de estas ferramentas serem úteis como instrumentos disciplinadores, elas não permitem ao docente refletir sobre a natureza social do plágio. Este trabalho desenvolve e instrumentaliza a hipótese de que o plágio, no contexto do ensino de programação, não acontece isoladamente da rede social formada pelos alunos em sala de aula. Em decorrência desta formulação propõe-se que os algoritmos de detecção de plágio e os códigos fonte apresentados durante uma disciplina serão suficientes para modelar indiretamente a rede social estabelecida pelo grupo de alunos. Um experimento exploratório foi conduzido para avaliar esta hipótese e um procedimento para intervenção inspirado nas técnicas de imunização de redes sociais foi proposto com o intuito de aumentar a eficácia da aprendizagem.

The field of social network analysis has expanded from the field of social science to the fields of human factors and ergonomics. There is a theory that suggest that one can use the social network methods and create an information network which describes the network from an information sharing perspective and and there are also theories which describes how social network analysis can be used study cognitive maps (mental models). This thesis touches both of these subjects in an attempt to investigate how social network analysis can be used together with real-time information as a data source to investigate the cognitive maps of individuals and comparing these maps with an organisations expected structure based on protocols. The study conducted showed that it was indeed possible to change the social network analysis method into an information based network which explains the origin of a mental model and to study information be- haviour, in a network, but there are still variables which needs to be studied further (e.g. failed information sharing and temporal aspects of information sharing).

L’entrepreneuriat en sciences humaines et sociales est un objet aux contours flous. On peut y regrouper un certain nombre d’activités économiques appelées selon les cas bureau d’études sociologiques, cabinet d’ingénierie territoriale, et, pour les personnes, historien-conseil, ethnologue-consultant ou sociologue-praticien. Cette thèse questionne les processus de création d’activités économiques dans des disciplines où on ne les attend pas : l’histoire, la géographie, la sociologie et l’ethnologie. Nous analysons l’évolution de la place de ces activités économiques selon les contextes historiques ainsi que les tensions auxquelles elles ont pu donner lieu dans les différents champs disciplinaires. Nous cherchons également à cerner ces structures et les personnes qui les composent en croisant diverses sources documentaires (fichiers Insee, répertoires professionnels, recherches de données en ligne). Enfin, au moyen d’entretiens biographiques (68 pour 50 cas d’entreprises), nous étudions les processus entrepreneuriaux et leurs ancrages dans des trajectoires d’entrepreneurs, des espaces sociaux, des réseaux de relations sociales et des dispositifs. Réseaux et dispositifs apparaissent comme des appuis pour l’entrée dans l’entrepreneuriat, comme des moyens d’accéder à divers types de ressources et comme des bases de coordination sur des marchés économiques. Dans un contexte peu structuré par des institutions professionnelles, les relations personnelles jouent un rôle particulièrement central dans la création des structures, dans la conduite des activités et dans les coordinations entre clients et entrepreneurs. / Entrepreneurship in social sciences is an object with blurred boundaries. One can group a number of economic activities known as sociological engineering, urban engineering, and for their members, consultant, public historian, ethnologist-consultant, professional sociologist... This thesis studies the process of economic activities in disciplines where it’s not usually expected: history, geography, sociology and ethnology. We analyze the evolution of the role of these economic activities according to historical contexts and the tensions which rise about them in the different disciplines. We also seek to identify structures and people in crossing various documentary sources (INSEE files, professional directories, online data research). Finally, using biographical interviews (62 to 50 cases of companies), we study the entrepreneurial process and their anchorages in entrepreneur life courses, social milieus, social networks and devices. Networks and devices appear to be the basis for entry into entrepreneurship, as means to access to various kinds of resources and as ways of coordination within economic markets. In an unstructured environment by professional institutions, personal relationships play a central role in the establishment of structures, in the activities and in the coordination between customers and entrepreneurs.

Nous sommes à l’aube d’une nouvelle ère du data mining, celle du stockage, traitement, analyse et exploitation des données massives que l’on appelle Big Data. Les données sont devenues une nouvelle matière première, très prisée par les entreprises de tout type et de toute taille à travers le monde ; elles permettent d’analyser, de comprendre, de modéliser et d’expliquer certains phénomènes comme le comportement et les préférences des utilisateurs ou clients d’une entreprise donnée. La compréhension des préférences des utilisateurs et des clients d’une entreprise permet de leur proposer de la publicité ciblée afin d’augmenter les ventes et la satisfaction des clients et ainsi pouvoir améliorer les revenues de l’entreprise, ce que les géants du Web comme Google, Facebook, LinkedIn et Twitter ont bien compris. Cette thèse de doctorat a été réalisée dans le cadre d’une convention CIFRE entre le laboratoire L2TI de l’université Paris 13 et la start-up franco-américaineWork4 qui développe des applications de recrutement sur Facebook. Son objectif principal était la mise au point d’un ensemble d’algorithmes et méthodes pour proposer aux utilisateurs des réseaux sociaux les offres d’emploi les plus pertinentes. Le développement de nos algorithmes de recommandation a nécessité de surmonter de nombreuses difficultés telles que le préservation de la vie privée des utilisateurs des réseaux sociaux, le traitement des données bruitées et incomplètes des utilisateurs et des offres d’emploi, la difficulté de traitement des données multi-langues et, plus généralement, la difficulté d’extraire automatiquement les offres d’emploi pertinentes pour un utilisateur donné parmi un ensemble d’offres d’emploi. Les systèmes développés durant cette thèse sont principalement basés sur les techniques de systèmes de recommandation, de recherche documentaire,de fouille de données et d’apprentissage artificiel ; ils ont été validés sur des jeux de données réels collectés par l’entreprise Work4. Dans le cadre de cette étude, les utilisateurs d’un réseau social sont liés à trois types entités : les offres d’emploi qui leur sont pertinentes, les autres utilisateurs du réseau social auxquels ils se sont liés d’amitié et les données personnelles qu’ils ont publiées sur leurs profils. Les profils des utilisateurs des réseaux sociaux et la description de nos offres d’emploi sont constitués de plusieurs champs contenant des informations textuelles. / We are entering a new era of data mining in which the main challenge is the storing andprocessing of massive data : this is leading to a new promising research and industry field called Big data. Data are currently a new raw material coveted by businesses of all sizes and all sectors. They allow organizations to analyze, understand, model and explain phenomen a such as the behavior of their users or customers. Some companies like Google, Facebook,LinkedIn and Twitter are using user data to determine their preferences in order to make targeted advertisements to increase their revenues.This thesis has been carried out in collaboration between the laboratory L2TI andWork4, a French-American startup that offers Facebook recruitment solutions. Its main objective was the development of systems recommending relevant jobs to social network users ; the developed systems have been used to advertise job positions on social networks. After studying the literature about recommender systems, information retrieval, data mining and machine learning, we modeled social users using data they posted on their profiles, those of their social relationships together with the bag-of-words and ontology-based models. We measure the interests of users for jobs using both heuristics and models based on machine learning. The development of efficient job recommender systems involved to tackle the problem of categorization and summarization of user profiles and job descriptions. After developing job recommender systems on social networks, we developed a set of systems called Work4 Oracle that predict the audience (number of clicks) of job advertisements posted on Facebook, LinkedIn or Twitter. The analysis of the results of Work4 Oracle allows us to find and quantify factors impacting the popularity of job ads posted on social networks, these results have been compared to those of the literature of Human Resource Management. All our proposed systems deal with privacy preservation by only using the data that social network users explicitly allowed to access to ; they also deal with noisy and missing data of social network users and have been validated on real-world data provided by Work4.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第16379号 / 工博第3460号 / 新制||工||1523(附属図書館) / 29010 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小林 潔司, 教授 川﨑 雅史, 准教授 松島 格也 / 学位規則第4条第1項該当

Research on social networks has established that those who bridge the gaps between dense social groups (i.e. structural holes) are granted a “vision advantage” compared to those who are embedded in dense groups. A common explanation for the advantage is that bridging a structural hole provides the broker with access to diverse information. What is less clear is how this process performs when the organizational context is turbulent. I propose that in a turbulent organizational context, when the organization is experiencing dramatic changes, employees benefit less from building a repertoire of diverse information and instead benefit more from adopting socially distant information. Information discussed by members of the organization which are several steps away from an employee would be more valuable in a turbulent context. Socially distant information would be more rare as people become rigid in response to threat, and it would be more relevant as local information becomes obsolete. To explore this idea, I study the case of two large organizations undergoing a merger integration. The members of the higher-status, acquiring organization experience relative stability compared to members of the target firm, who experience a great deal more uncertainty. The higher-status firm dominated the merger, the top management of the target firm was replaced, supervisory structures are changed, employees are forced to develop new routines, learn new technologies, and had to uproot their social support networks and move across the country. This case provides an opportunity to examine how two information flow mechanisms, which mediate the relationship between broker positions and individual career benefits, are altered in the presence of organizational turbulence. I measure information variance and the adoption of socially distant information of 612 organizational members by fitting a topic model on a dataset of email content covering a 14-month period immediately following the merger of two large consumer product firms. I test my hypotheses using a latent difference score model to test the impacts of increases in information variance, constraint, and adoption of socially distant information on increases in employee salary. I find that organizational turbulence alters the ways in which information flows provides benefits. Within turbulent contexts the pathways between access to diverse information and improved career outcomes are destroyed. Instead adopting socially distant information and information associated with power and status provides more benefits to the individual than incrementally improving a repertoire of diverse information. This study contributes to research on M&As, organizational change, and social network theory by expanding our understanding of the impact of organizational turbulence on the information mechanisms driving advantage in open networks.

Le marketing viral est une nouvelle forme de marketing qui exploite les réseaux sociaux afin de promouvoir un produit, une marque, etc. Il se fonde sur l'influence qu'exerce un utilisateur sur un autre. La maximisation de l'influence est le problème scientifique pour le marketing viral. En fait, son but principal est de sélectionner un ensemble d'utilisateurs d'influences qui pourraient adopter le produit et déclencher une large cascade d'influence et d'adoption à travers le réseau. Dans cette thèse, nous proposons deux modèles de maximisation de l'influence sur les réseaux sociaux. L'approche proposée utilise la théorie des fonctions de croyance pour estimer l'influence des utilisateurs. En outre, nous introduisons une mesure d'influence qui fusionne de nombreux aspects d'influence, comme l'importance de l'utilisateur sur le réseau et la popularité de ces messages. Ensuite, nous proposons trois scénarii de marketing viral. Pour chaque scénario, nous introduisons deux mesures d'influence. Le premier scénario cherche les influenceurs ayant une opinion positive sur le produit. Le second scénario concerne les influenceurs ayant une opinion positive et qui influencent des utilisateurs ayant une opinion positive et le dernier scénario cherche des influenceurs ayant une opinion positive et qui influencent des utilisateurs ayant une opinion négative. Dans un deuxième lieu, nous nous sommes tournés vers un autre problème important, qui est le problème de la prédiction du sujet du message social. En effet, le sujet est également un paramètre important dans le problème de la maximisation de l'influence. A cet effet, nous introduisons quatre algorithmes de classification qui ne nécessitent pas le contenu du message pour le classifier, nous avons juste besoin de ces traces de propagation. Dans nos expérimentations, nous comparons les solutions proposées aux solutions existantes et nous montrons la performance des modèles de maximisation de l'influence et les classificateurs proposés. / The Viral Marketing is a relatively new form of marketing that exploits social networks in order to promote a product, a brand, etc. It is based on the influence that exerts one user on another. The influence maximization is the scientific problem for the Viral Marketing. In fact, its main purpose is to select a set of influential users that could adopt the product and trigger a large cascade of influence and adoptions through the network. In this thesis, we propose two evidential influence maximization models for social networks. The proposed approach uses the theory of belief functions to estimate users influence. Furthermore, we introduce an influence measure that fuses many influence aspects, like the importance of the user in the network and the popularity of his messages. Next, we propose three Viral Marketing scenarios. For each scenario we introduce two influence measures. The first scenario is about influencers having a positive opinion about the product. The second scenario searches for influencers having a positive opinion and influence positive opinion users and the last scenario looks for influencers having a positive opinion and influence negative opinion users. On the other hand, we turned to another important problem which is about the prediction of the social message topic. Indeed, the topic is also an important parameter in the influence maximization problem. For this purpose, we introduce four classification algorithms that do not need the content of the message to classify it, they just need its propagation traces. In our experiments, we compare the proposed solutions to existing ones and we show the performance of the proposed influence maximization solutions and the proposed classifiers.