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Video game streaming has introduced to consumers a new method of creating branded content. Popular streaming platforms receive millions of broadcasters and viewers every month, and the current examines the influence of this type user-generated content on consumer attitudes and behaviors. The goal of this study is to understand how video game streams function as a marketing tool. To investigate this, a quantitative survey was designed and measured participants’ video gaming habits and their perceptions of credibility, usefulness of content, group identification, and purchase intention. Heavier gaming habits were found to be positively related to perceived credibility in a user-generated stream condition. Group identification and stream familiarity were found to be positively related to perceived credibility. These findings hold implications for using video game streams as a marketing tool, as heavier gamers were found perceive user-generated streams as a credible source of information.

The aim of this study is to examine Japanese gendered language use in online game streaming, and the differences in gendered language use between male and female streamers. The main aim of this thesis is to examine how, and to what extent, young Japanese adults use gendered language when broadcasting gaming streams online. I will examine how pronounced the differences in gendered language use are between male and female streamers, and see if the major theories about gendered language apply in the Japanese streaming community. To collect the data, I looked at 20 game streamers, with each stream lasting 15-30 minutes. I transcribed the streamers’ commentary, and examined the frequency of certain sentence ending particles, personal pronouns, and polite speech/word choice. The streamers were chosen with regards given to certain criteria to prevent skewed results, and to control the independent variables to a certain extent. For example, the streamers must be playing alone to ensure that the streamer is the only person speaking. I analyzed the results using a qualitative method, which indicated that the greater gender differences are found in the use of personal pronouns, but not as much in the use of sentence ending particles or politeness level.

<p>With the popularity of the gaming industry, game streaming appeared and became a global phenomenon with high participation in recent years. Game streaming platforms such as Twitch had millions of active users participated in the community by watching and chatting. Yet there was lack of investigation about how chat behaviors connected with the overall participation in game streaming community. This study aims to describe and analyze the roles taken on by viewers as they engaged in chat while watching game streaming and identify how these roles influenced participation. I designed a qualitative study with online observations on several Twitch channels streaming Overwatch. By analyzing the chatlogs collected, I identified four social roles among chatters: Lurker, Troll, Collaborator, and Moderator. A discourse analysis was applied to further investigate the interactions among these roles and how they shape the conversation in chatrooms. With these findings, I generated a four-role model that specific for chatters in Twitch personal channels. Limitations of this study and suggestions for future research were also provided.</p>

Submitted by Rosa Assis (rosa_assis@yahoo.com.br) on 2017-11-14T18:05:03Z No. of bitstreams: 2 VICTOR PERAZZOLO BARROS.pdf: 24134660 bytes, checksum: 8761000aa9ba093f81a14b3c2368f2b7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Paola Damato (repositorio@mackenzie.br) on 2017-11-27T12:14:38Z (GMT) No. of bitstreams: 2 VICTOR PERAZZOLO BARROS.pdf: 24134660 bytes, checksum: 8761000aa9ba093f81a14b3c2368f2b7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-11-27T12:14:38Z (GMT). No. of bitstreams: 2 VICTOR PERAZZOLO BARROS.pdf: 24134660 bytes, checksum: 8761000aa9ba093f81a14b3c2368f2b7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-02-06 / The advances in Cloud Computing and communication technologies enabled the concept of Cloud Gaming to become a reality. Through PCs, consoles, smartphones, tablets, smart TVs and other devices, people can access and use games via data streaming, regardless the computing power of these devices. The Internet is the fundamental way of communication between the device and the game, which is hosted and processed on an environment known as Cloud. In the Cloud Gaming model, the games are available on demand and offered in large scale to the users. The players' actions and commands are sent to servers that process the information and send the result (reaction) back to the players. The volume of data processed and stored in these Cloud environments exceeds the limits of analysis and manipulation of conventional tools, but these data contains information about the players' profile, its singularities, actions, behavior and patterns that can be valuable when analyzed. For a proper comprehension and understanding of this raw data and to make it interpretable, it is necessary to use appropriate techniques and platforms to manipulate this amount of data. These platforms belong to an ecosystem that involves the concepts of Big Data. The model known as Big Data Analytics is an effective and capable way to, not only work with these data, but understand its meaning, providing inputs for assertive analysis and predictive actions. This study searches to understand how these technologies works and propose a method capable to analyze and identify patterns in players' behavior and characteristics on a virtual environment. By knowing the patterns of different players, it is possible to group and compare information, in order to optimize the user experience, revenue for developers and raise the level of control over the environment in a way that players' actions can be predicted. The results presented are based on different analysis modeling using the Hadoop technology combined with data visualization tools and information from open data sources in a dataset of the World of Warcraft game. Fraud detection, users' game patterns, churn prevention inputs and relations with game attractiveness elements are examples of modeling used. In this research, it was possible to map and identify the players' behavior patterns and create a prediction of its frequency and tendency to evade or stay in the game. / Os avanços das tecnologias de Computacão em Nuvem (Cloud Computing) e comunicações possibilitaram o conceito de Jogos em Nuvem (Cloud Gaming) se tornar uma realidade. Por meio de computadores, consoles, smartphones, tablets, smart TVs e outros equipamentos é possível acessar via streaming e utilizar jogos independentemente da capacidade computacional destes dispositivos. Os jogos são hospedados e executados em um ambiente computacional conhecido como Nuvem, a Internet é o meio de comunicação entre estes dispositivos e o jogo. No modelo conhecido como Cloud Gaming, compreendesse que os jogos são disponibilizados sob demanda para os usuários e podem ser oferecidos em larga escala. Os comandos e ações dos jogadores são enviados para servidores que processam a informação e enviam o resultado (reação) para o jogador. A quantidade de dados que são processados e armazenados nestes ambientes em Nuvem superam os limites de análise e manipulação de plataformas convencionais, porém tais dados contém informacões sobre o perfil dos jogadores, suas particularidades, ações, comportamentos e padrões que podem ser importantes quando analisados. Para uma devida compreensão e lapidação destes dados brutos, a fim de torná-los interpretáveis, se faz necessário o uso de técnicas e plataformas apropriadas para manipulação desta quantidade de dados. Estas plataformas fazem parte de um ecossistema que envolvem os conceitos de Big Data. Arquiteturas e ferramentas de Big Data, mais especificamente, o modelo denominado Big Data Analytics, são instrumentos eficazes e capazes de não somente trabalhar com estes dados, mas entender seu significado, fornecendo insumos para análise assertiva e predição de acões. O presente estudo busca compreender o funcionamento destas tecnologias e fornecer um método capaz de identificar padrões nos comportamentos e características dos jogadores em ambiente virtual. Conhecendo os padrões de diferentes usuários é possível agrupar e comparar as informações, a fim de otimizar a experiência destes usuários no jogo, aumentar a receita para os desenvolvedores e elevar o nível de controle sobre o ambiente ao ponto que seja possível de prever ações futuras dos jogadores. Os resultados obtidos são derivados de diferentes modelagens de análise utilizando a tecnologia Hadoop combinada com ferramentas de visualização de dados e informações de fontes de dados abertas, em um dataset do jogo World of Warcraft. Detecção de fraude, padrões de jogo dos usuários, insumos para prevencão de churn e relações com elementos de atratividade no jogo, são exemplos de modelagens abordadas. Nesta pesquisa foi possível mapear e identificar os padrões de comportamento dos jogadores e criar uma previsão e tendência de assiduidade sobre evasão ou permanencia de usuários no jogo.