Understanding the growth pattern of content popularity has become a subject of immense interest to Internet service providers, content makers and on-line advertisers. This understanding is also important for the sustainable development of content distribution systems. As an approach to comprehend the characteristics of this growth pattern, a significant amount of research has been done in analyzing the popularity growth patterns of YouTube videos. Unfortunately, no work has been done that intensively investigates the popularity patterns of YouTube videos based on video object category. In this thesis, an in-depth analysis of the popularity pattern of YouTube videos is performed, considering the categories of videos. Metadata and request patterns were collected by employing category-specific YouTube crawlers. The request patterns were observed for a period of five months. Results confirm that the time varying popularity of di fferent YouTube categories are conspicuously diff erent, in spite of having sets of categories with very similar viewing patterns. In particular, News and Sports exhibit similar growth curves, as do Music and Film. While for some categories views at early ages can be used to predict future popularity, for some others predicting future popularity is a challenging task and require more sophisticated techniques, e.g., time-series clustering. The outcomes of these analyses are instrumental towards designing a reliable workload generator, which can be further used to evaluate diff erent caching policies for YouTube and similar sites. In this thesis, workload generators for four of the YouTube categories are developed. Performance of these workload generators suggest that a complete category-specific workload generator can be developed using time-series clustering. Patterns of users' interaction with YouTube videos are also analyzed from a dataset collected in a local network. This shows the possible ways of improving the performance of Peer-to-Peer video distribution technique along with a new video recommendation method.

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[pt] As novas tecnologias da informação e comunicação, associadas à perda de protagonismo das mídias de massa tradicionais, impuseram às campanhas eleitorais novas formas de estruturação, de modo a atender às demandas de uma população cada vez mais habituada a consumir informação de forma rápida e simplificada. A necessária reprogramação da comunicação política fez surgir o que se entende como campanhas orientadas por dados, caracterizadas pela utilização de grandes volumes de dados pessoais na elaboração das estratégias de propaganda eleitoral. No Brasil, a positivação do impulsionamento de conteúdo on-line, em conjunto com a prática de difusão em massa de conteúdo eleitoral por aplicativos de mensagens instantâneas – observada de forma contundente nas eleições de 2018 – indicam a consolidação das campanhas orientadas por dados no cenário político-eleitoral nacional e as recentes reformas na Lei de Eleições revelam a intenção do legislador em adequar o regramento eleitoral à nova realidade das campanhas. As alterações legislativas, no entanto, não vieram acompanhada na necessária preocupação em promover uma ampla atualização da legislação eleitoral para fazer frente aos riscos que as práticas adotadas pelas campanhas orientadas por dados podem acarretar ao processo democrático, principalmente no tocante à autonomia do eleitor e à proteção de dados pessoais, demandando maiores estudos sobre o tema. / [en] New informational and communication technologies associated with traditional mass media s faltering prominence precipitated new methods of structuring electoral campaigns to meet the demands of a population increasingly used to consuming information in a fast and simplified way. The required reprogramming of political communication gave rise to what are understood as data-driven campaigns,characterized by the use of large volumes of personal data to drive electoral propaganda strategies. In Brazil, the legalization of online content boosting, along side mass dissemination of electoral content through instant messaging applications – strikingly observed in the 2018 elections – indicate the consolidation of data-driven campaigns inthe national political-electoral environment. Further, recent reforms in the Electoral Lawreveal legislators intent to adapt electoral rules to the new reality presented by these campaigns. The changes, however, were not accompanied by a much-needed, broader investigation and update of the electoral legislation to confront the risks that these data driven campaign practices bring to the democratic process, especially with regard to voter autonomy and protection of personal data.