Na Web 2.0 são encontrados sistemas com alto volume de interação social. Alguns desses sistemas oferecem cálculo de reputação ou alguma forma de classificação de usuários ou do conteúdo compartilhado. Contudo, em muitos casos, esse valor de reputação resultante é obtido somente a partir de dados quantitativos ou qualitativos. O objetivo deste trabalho é elaborar um modelo para o cálculo de reputação em comunidades on-line, baseando-se em dados qualitativos e quantitativos provenientes da interação dos próprios participantes da rede, a fim de potencializar a colaboração entre os membros e fornecer um meio de cálculo resistente a algumas das vulnerabilidades comuns em sistemas de reputação, como tolerância a ruídos e ataques Sybil. Para atingir esse objetivo é realizada uma adaptação do algoritmo PageRank, definida como CR (Collaborative Reputation) para obter uma ordenação dos usuários a partir de suas interações. Para avaliação, adotamos um conjunto de dados do sítio Epinions.com, com o qual foi realizada uma análise comparativa dos resultados obtidos a partir do modelo proposto com outros três algoritmos correlatos ao trabalho apresentado. Dentre as técnicas usadas na análise estão: diversidade de valores, comparação da ordenação, estudo comparativo de cenários, tolerância a ruídos e robustez contra ataques tipo Sybil. Os algoritmos usados na avaliação são: o PageRank original e o algoritmo ReCop, usados para a identificação de usuários relevantes, e o algoritmo LeaderRank usado para a identificação dos usuários com maior prestígio na rede. Os resultados indicam que o modelo proposto é mais sensível às interações dos usuários em comparação aos outros modelos usados na avaliação, mas é mais eficiente a ataques Sybil. / In the Web 2.0, there are systems with high volume of social interaction. Some of these systems offer reputation calculation or some form of classification of users or shared content,. However, in many cases, this reputation value is obtained solely from quantitative or qualitative data. The objective of this work is to develop a model for the reputation calculation in online communities, based on qualitative and quantitative data from the interaction of the participants of a social network, in order to potentiate the collaboration between users, and to provide a resistant environment for some of the vulnerabilities present in reputation systems. To achieve this goal we defined an adaptation of the PageRank algorithm, defined as CR (Collaborative Reputation), to obtain a rank of users based on their interactions in the network. For evaluation, we used a dataset from the site Epinions.com. With that database, we executed a comparative analysis of the results of the proposed algorithm and of three other algorithms related to the presented work. The procedures used in the analysis were: diversity of values, comparison of ordination, comparative study of scenarios, noise tolerance and robustness against Sybil attacks. The algorithms used in the comparison were: the original PageRank algorithm and ReCop, used to identify relevant users, and the algorithm LeaderRank, which is used for identification of the most prestigious users in the network. The results showed that the proposed model is more sensitive to the interactions of users, but its performance on Sybil attacks is better than the others.

A Web 2.0 vem alterando o comportamento do consumidor em relação às marcas. No contexto de interação pelas comunidades de marcas, sites de opiniões, blogs e micro blogs, aflora a prática do colaborativismo em que consumidores, espontaneamente, tomam atitudes de recomendar online bens e serviços, baseados em suas experiências positivas ou negativas de pré-compra. Estas atitudes de recomendação online fazem parte de um novo fenômeno que tem sido pesquisado no âmbito do estudo da intenção de compra do consumidor. O papel deste consumidor como colaborador e gerador de recomendações espontâneas nas mídias sociais traz um aumento da importância da fase de consideração no tradicional funil de compras, alterando o caminho que o consumidor decide por suas compras de produtos e serviços. Como resultado disso um impacto positivo ou negativo pode ser atribuído à intenção de compra e seus antecedentes como o sentimento de confiança, a expectativa de satisfação e a intenção de recomendação. Neste trabalho, propõe-se um framework teórico para discutir o impacto dessas mudanças e o desenho e a análise dos resultados de um experimento fatorial 2x2x2 que contribui para determinar qual o efeito das recomendações positivas ou negativas de influenciadores nas mídias sociais sobre esses antecedentes e a intenção de compra do consumidor. Como foco do experimento investiga-se e mensura-se o tamanho do impacto sobre dois tipos de serviços distintos em termos de valor para o cliente, um de alto valor monetário e outro de baixo valor monetário para o consumidor. De forma complementar investiga-se a existência ou não de diferença de atitude do consumidor em relação à intenção de compra destes serviços, entre recomendações originárias dos grupos pertencentes ao seu relacionamento social ou não. O resultado do estudo evidencia que o único fator relevante que traz impacto tanto para os antecedentes como para a intenção de compra do consumidor é o tipo de recomendação - positiva ou negativa - e que, de uma forma geral, maior impacto é atribuído para as recomendações positivas. O estudo proposto apresenta contribuições ao entendimento do comportamento do consumidor frente a este novo ambiente e trabalha no sentido de elucidar discussões de caráter gerencial com o foco em serviços em relação a esforços de marketing necessários em uma nova realidade no cenário corporativo, a era da colaboração e da importância crescente do papel dos influenciadores. / The Web 2.0 is changing consumer behavior towards brands. In the context of interaction through brand communities, reviews sites, blogs and microblogs, arises the practice of collaboration in which consumers spontaneously take attitudes to recommend online goods and services, based on their positive or negative experiences of pre-purchase. These attitudes of online recommendation belong to a new phenomenon that has been researched in the study of consumer purchase intention. The role of this consumer as a collaborator and generating recommendations spontaneously in the social media provides an increase of the importance of the stage of consideration in the traditional purchasing funnel, changing the way that consumers decide by their purchases of products and services. As a result, a positive or negative impact can be attributed to the purchase intention and its antecedents such as trust, satisfaction and intention of service recommendation . In this study, we propose a theoretical framework to discuss the impact of these changes and the design and analysis of a 2x2x2 factorial experiment that contributes to determine what is the effect of positive or negative recommendations from social media influencers on antecedents and consumer purchase intention. As focus of that experiment we have manipulated two types of services (low and high price), peer groups (personal and external) and nature of message (negative and positive recommendation). The results show that the only relevant factor which brings impact on the trust, satisfaction, recommendation and purchase intention is the type of message - positive or negative - and in general greater impact is attributed to positive messages. This study contributes to the understanding of the consumer behavior face this new environment and can drive to discuss the marketing efforts required in a new reality in the corporate scenario - the era of collaboration and increasing role of influencers.

INTRODUCTION: Human Immunodeficiency Virus (HIV) infection remains prevalent among the minority and drug using population in the United States. Testing for HIV is an important and cost effective way to reduce HIV prevalence. OBJECTIVE: To assess the HIV testing behavior of people who use non-injected drugs (PWND) and compare it to that of people who use injected drugs (PWID), in order to determine which factors, in terms of social context as well as individual risks, predict HIV testing among the PWND. METHOD: A cross-sectional study of HIV testing behavior of PWND compared to PWID was conducted and the data was analyzed by applying negative binomial regression models. Then, a negative binomial regression using generalized estimating equation (GEE) was employed in order to identify the predictive factors for HIV testing among PWND over a 2-year period. RESULTS: Individuals who reported using injected drugs tended to undergo HIV tests more often compared to those who used non-injected drugs, PR (95% CI) = 1.24 (1.02, 1.51), p = 0.03. The interaction term between injection status and emotional support in relation to HIV testing was significant, 0.75 (0.59, 0.97), p = 0.03. PWID that had access to greater emotional support on average tended to test for HIV less frequently than did PWID with less emotional support. In stratified analyses, emotional support was negatively associated with testing among PWID and positively associated among PWND, though both relationships were borderline significant. HIV testing among users of illicit drugs was dependent on emotional support. According to the GEE models examining the factors predicting HIV testing among PWND, sexually transmitted infections, non-injected heroin use, being in drug treatment, engagement in sexual transactions, and instability in drug networks were the main factors contributing to being HIV tested, as well as frequency of testing. The positive influence of emotional support on these variables was borderline significant. CONCLUSION: People who use non-injected drugs are less likely to test for HIV compared to those who use injected drugs, though they may share similar risk factors for HIV transmission and acquisition. To exert a greater impact on the HIV epidemic, interventions and policies encouraging HIV testing in this subpopulation, which remains under-recognized by both researchers and health practitioners in terms of the potential risks for contracting the HIV, are warranted.

This dissertation focuses on understanding and supporting individual and collective sensemaking within online health communities (OHCs). This major goal was achieved in three aims. In Aim 1, this dissertation contributes a rich descriptive account of collective sensemaking in OHCs forums by describing how it occurs and develops, what triggers it, what elements constitute collective construction of meaning, and what conversational moves positively contribute to this process. Further, it describes how collective sensemaking in OHCs is impacted by the interplay between informational and socio-emotional needs of OHCs members. Moreover, it examines how design of different social computing platforms influences OHCs members’ ability to meet their informational and socio-emotional needs and engage in collective sensemaking. In Aim 2, this dissertation explores the design space of tools for supporting individual sensemaking through optimized information access. Through the design and evaluation of a prototype DisVis it examines the impact of such tools on OHCs members’ ability to understand information within discussion threads. In the final Aim 3, this dissertation proposes a novel approach for meeting the three main needs identified in Aims 1 and 2: promoting individual sensemaking, while at the same time encouraging collective sensemaking, and facilitating development of social awareness and ties among community members. The design and evaluation of the novel solution for visualizing discussion threads that synergistically addresses these three needs—dSense—provides insights for future research and design of interactive solutions for supporting individual and collective sensemaking within OHCs.

Este trabalho investigou a relação de alunos do ensino fundamental com os memes na internet. Apropriados em larga escala por crianças e adolescentes e circulando de forma intensa nas redes sociais, as montagens e remodelagens dos memes -- conhecidas na linguagem digital como \"zoeira\" -- passaram a ocupar lugar de destaque no cotidiano juvenil. A partir de pesquisa de campo realizada na Escola Municipal Julio Marcondes Salgado, zona norte da cidade de São Paulo, desenvolvemos estudo qualitativo sobre as funções dos memes -- uma das principais expressões narrativas da internet --, discutindo as representações e os sentidos atribuídos pelos discentes a esses novos produtos culturais. Também apresentamos, como parte dos resultados, uma proposta de gêneros e subgêneros para categorizar como os memes se estabelecem enquanto linguagem. Com um prisma teórico articulado entre os estudos de Educomunicação, cibercultura e o cotidiano juvenil, o objetivo é discutir o que são os memes, como eles sintetizam as mudanças em trânsito na sociedade contemporânea e, consequentemente, os possíveis desdobramentos da nova realidade comunicacional no universo escolar. / This work investigated the relationship of young students with memes in digital culture. Appropriated on a large scale by children and adolescents and circulating intensively in social networks, memes became a central element in youth routine. From a field research carried out at the Julio Marcondes Salgado Municipal School, north of the city of São Paulo (Brazil), we developed a qualitative study on the functions of memes -- one of the main narrative expressions on the Internet --, discussing the representations and meanings attributed by the students for these new cultural products. We also present, as part of the results, a proposal of genres and subgenres to categorize how memes works as a language. With a theoretical prism articulated between the studies of Educommunication, cyberculture and youth culture, the objective is to discuss what memes are, how they synthesize the changes in transit in contemporary society and, consequently, the possible unfolding of this new communicational reality to the educational routine.

A identificação de especialistas consiste na análise automática de informações sobre um conjunto de indivíduos para a localização daqueles com maior conhecimento em determinado tópico. Seus conhecimentos podem ser aplicados para a melhoria da produtividade em projetos de trabalho ou pesquisa e na localização de pesquisadores capacitados para avaliarem trabalhos de pesquisa ou para compor bancas de mestrado e doutorado. Para isso é interessante uma boa fonte de informações sobre os candidatos, de forma a otimizar o processo de identificação. No Brasil existe uma base de currículos que centraliza as informações sobre os pesquisadores brasileiros, a Plataforma Lattes. Os dados dessa plataforma são utilizados nesse trabalho para a realização da identificação de especialistas de acordo com suas áreas de atuação. São coletados dados da Plataforma e realizadas três etapas para compor uma lista ordenada dos principais especialistas de determinada área identificados pelo algoritmo proposto. A primeira etapa é a utilização da métrica TF-IDF, de modo que seus resultados são combinados com os estratos Qualis das publicações de cada autor, para finalmente ser aplicado um algoritmo de propagação para a geração da lista de especialistas. Após uma extensiva otimização dos parâmetros envolvidos no algoritmo, os resultados foram comparados com outra técnica existente que foi utilizada como base para esse projeto. Os resultados foram validados a partir de três conjuntos de pesquisadores coletados para esse projeto. O primeiro deles envolveu os pesquisadores contemplados com a Bolsa Produtividade em Pesquisa CNPq. Os resultados de dois questionários envolvendo pesquisadores das áreas de Inteligência Artificial e Nanotecnologia também foram utilizados. A partir dos resultados obtidos, foi possível notar que a técnica proposta aprimora significativamente os resultados do conjunto de Bolsistas Produtividade. Por outro lado, os resultados obtidos para os conjuntos de pesquisadores de Inteligência Artificial e Nanotecnologia foram ligeiramente melhores para a abordagem base. Os dados, algoritmos e resultados obtidos nesse trabalho poderão ser utilizados futuramente para aprimorar a identificação de especialistas / Expert finding consists in the automatic analysis of informations about a set of people with the objective of finding those with greater knowledge on a particular topic. His knowledge can be applied to improve the productivity in research and work projects and to locate researchers who are capable of evaluating research papers or for masters and doctoral degrees. For this objective it\'s interesting a good source of information about the candidates, in order to optimize the process of identification. In Brazil there\'s a curriculum base that centralizes the information about Brazilian researchers, the Lattes Plataform. It\'s data is used in this work to perform the expert finding according to the candidate\'s areas of activity. Data are collected from the Platform and three steps performed to generate a ordered list of the main specialists of a given area identified by the proposed algorithm. The first step is to use the TF-IDF metric, so that its results are combined with the Qualis strata from the publications of each author, to finally be applied a propagation algorithm for the generation of the specialists list. After an extensive optimization of the parameters involved in the algorithm, the results were compared with another technique that was used as the basis for this project. The results were validated from three sets of researchers collected for this project. The first one involved the researchers contemplated with the CNPq Productivity Grant. The results of two questionnaires involving researchers from the areas of Artificial Intelligence and Nanotechnology were also used. From the results obtained, it was noticeable that the proposed technique significantly improves the results of the Productivity Grant set. On the other hand, the results obtained for the Artificial Intelligence and Nanotechnology research groups were slightly better for the base approach. The data, algorithms and results obtained in this work may be used in the future to improve the expert finding

Geographic clusters of firms have been extensively studied in different bodies of literature, but little attention has been paid to the process of cluster formation and its determinants. While focusing on the effects of clusters on innovations and on the productivity of firms, the literature has neglected the agency of entrepreneurs in cluster emergence. This thesis aims to contribute to the literature on clusters by studying the role of personal networks and firm networks in three aspects of the emergence of clusters: 1) the early stages of formation; 2) the creation of business relations between firms; and 3) the creation of knowledge among clustered firms. The analysis was conducted using the biotechnology sector in four geographic regions of Chile as a case study. Data on firms was collected using in-depth interviews and a survey. The analysis of the data was carried out using content analysis, multilevel estimations and econometric analysis. The results reveal three main findings. First, the personal and business relations of entrepreneurs can determine the location decisions of firms. Second, personal relations are positively associated with the emergence of formal business relations between firms. Third, the number of personal and business connections a firm has positively affects its production of knowledge, measured as patent applications and scientific journals. These findings suggest that social networks within a cluster shape its emergence and development. The results also show that the different types of networks coexisting in a cluster - personal networks, business networks and research networks, among others - affect one another and determine the development of clusters. The implications of this research may be helpful for policy-makers, professional associations and cluster managers. Activities to foster personal interaction between members of a cluster and other key actors - universities, incubators, venture capital firms, government agencies, etc. - may generate collaborations between firms that would not otherwise emerge.

With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems.

Sentiment Analysis has been researched in a variety of contexts but in this thesis, the focus is on sentiment analysis in Twitter, which poses its own unique challenges such as the use of slang, abbreviations, emoticons, hashtags, and user mentions. The 140-character restriction on the length of tweets can also lead to text that is difficult even for a human to determine its sentiment. Specifically, this study will analyze sentiment analysis of bilingual (U.S. English and Spanish language) Tweets. The hypothesis here is that Bilingual sentiment analysis is more accurate than sentiment analysis in a single language (English or Spanish) when analyzing bilingual tweets. In general, currently sentiment analysis in bilingual tweets is done against an English dictionary. For each of the test cases in this thesis’ experiment we will use the Python NLTK sentiment package. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

I investigate whether or not a firm’s social network size (also known as social capital) impacts the quality of its information environment. Following social capital theory, I posit three potential channels that help bring an informational advantage to wellconnected firms. First, well-connected firms are likely to have timely access to a broader set of information that affords them the opportunity to disclose this information. Second, a social network fosters trust among social peers, which promotes the transfer of more accurate information within that network. Third, well-connected executives and directors have greater reputational capital at stake, which may encourage them to provide accurate information to the market. I provide evidence that well-connected firms have higher quality information environments. I further document that the beneficial impact of the firm’s social network size on the quality of the firm’s information environment is higher for complex firms. I also find that the beneficial effect of the firm’s social ties on the quality of the firm’s information environment is greater when the firm’s connections are in the same industry or are top executives or are industry leaders or are financiers in the capital markets. My study extends existing social network literature by investigating whether firm’s social connections to outside executives and directors impact the quality of the firm’s information environment. My paper focuses on the networking skills of the executives and directors and extends the literature on how executives’ and directors’ personal characteristics are important. Additionally, I respond to the call by Engelberg et al. (2013) to identify the mechanism by which a CEO’s network creates value to the firm and well-connected CEOs get paid higher compensation. This study also contributes to a growing debate in social network literature between social capital theory and agency theory. Finally, my study is important to the regulators and standard setters as they can provide further evidence on the impact of non-financial information on the information quality surrounding the firm. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection