Analyzing mobile games using a social network analysis approach
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Date
2014
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University of New Brunswick
Abstract
In recent times, analysis of user-generated data acquired from social media has proven to be beneficial in helping organizations make decisions about their businesses. This forms a basis for exploring other areas where social network analysis might be useful. In this report, I decided to look at the mobile games industry and see how accurate social network analysis can be, in making predictions of possible real world outcomes. Three approaches considered were how often a game is mentioned in social media (Frequency Count), sentiments attached to each game (Sentiment Analysis), and a game’s position with other games in the network (Centrality Measures). Furthermore,using multiple linear regression analysis, five different predictive models were created by combining the three approaches. Finally, an evaluation of these approaches was done by performing correlation analysis between the rankings produced by each approach with the rankings in the Google Playstore. The best approach had a correlation coefficient of 0.58, which meant that the predictive ability of social network analysis for this industry is moderate.