Knowledge extraction from the behaviour of players in a web browser game

Dissertação de mestrado em Informatics EngineeringThe analysis of the player’s behaviour is a requirement with growing popularity in the traditional computer games segment and has been proven to aid the developers create better and more profitable games. There is now interest in trying to replicate this attainment in a less conventional genre of games known as web browser games. The main objective of this work is to analyse and create a technique for the analysis of the behaviour of the players inside a web browser game. For this analysis a system to automatically collect, process and store the relevant data for the referred analysis was developed. The web browser game used as a case study for this work is developed by 5DLab and is called Wack-a- Doo. The work developed focused on creating short-term prediction models using the information collected during the first days of playing for each player. The objectives of these models are to predict the time played or the conversion state of the players. With the study of the created models it was possible to extract results that provide potentially useful information to increase the profitability of Wack-a-Doo.A análise do comportamento de jogadores é uma prática com crescente popularidade no segmento dos jogos de vídeo tradicionais. Esta técnica foi já aprovada como capaz de ajudar os criadores a desenvolver melhores e mais lucrativos jogos. Existe agora interesse em tentar replicar este sucesso num género de jogos de vídeo menos convencionais normalmente referidos como jogos de browser web. O objetivo deste trabalho é analisar e criar uma técnica para essa análise do comportamento dos jogadores de um jogo de browser web. Para isto um sistema automático de recolha, processamento e armazenamento dos dados relacionados com o comportamento dos jogadores foi desenvolvido. O jogo de browser web usado para este estudo foi criado pela empresa 5DLab e dá pelo nome de Wack-a-Doo. O trabalho desenvolvido centrou-se em fazer modelos de previsão de curto prazo usando as informações recolhidas durante os primeiros dias de jogo de cada jogador. Estes modelos têm como objetivo prever o tempo jogado e o estado de conversão do jogador. Estudando os modelos criados foi possível extrair resultados que fornecem informação potencialmente útil para melhorar a rentabilidade do Wack-a-Doo

Assessing Influential Users in Live Streaming Social Networks

abstract: Live streaming has risen to significant popularity in the recent past and largely this live streaming is a feature of existing social networks like Facebook, Instagram, and Snapchat. However, there does exist at least one social network entirely devoted to live streaming, and specifically the live streaming of video games, Twitch. This social network is unique for a number of reasons, not least because of its hyper-focus on live content and this uniqueness has challenges for social media researchers. Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do? This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.Dissertation/ThesisDoctoral Dissertation Computer Science 201