Analyzing mobile games using a social network analysis approach

dc.contributor.advisorDu, Weichang
dc.contributor.advisorDu, Donglei
dc.contributor.authorUmar, Nimat Onize
dc.description.abstractIn 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.
dc.description.copyrightNot available for use outside of the University of New Brunswick
dc.description.noteElectronic Only (UNB thesis number) Thesis 9501 (OCoLC)958877849
dc.description.noteM.C.S University of New Brunswick, Faculty of Computer Science, 2014.
dc.format.extentix, 69 pages
dc.identifier.otherThesis 9501
dc.publisherUniversity of New Brunswick
dc.subject.disciplineComputer Science
dc.subject.lcshMobile games industry.
dc.subject.lcshOnline social networks.
dc.titleAnalyzing mobile games using a social network analysis approach
dc.typemaster thesis Science of Computer Science of New Brunswick


Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
1.16 MB
Adobe Portable Document Format