Predicting iPhone Sales from iPhone Tweets

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings

Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating are proxies for
user/consumer’s attention to a particular object/product and that the shared digital artefact that is persistent can create social influence. In this paper, we demonstrate how social media data from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a linear regression model that transforms iPhone tweets into a prediction of the quarterly iPhone sales with an average error close to the established prediction models from investment banks. This strong correlation between iPhone tweets and iPhone sales becomes marginally stronger after incorporating sentiments of tweets. We discuss the findings and conclude with implications for predictive analytics with big social data.

Publication information

Original languageEnglish
Title of host publicationProceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014
EditorsManfred Reichert, Stefanie Rinderle-Ma, Georg Grossmann
Number of pages10
Place of PublicationLos Alamitos, CA
Publication date2014
ISBN (Print)9781479954704
StatePublished - 2014
EventThe 18th IEEE Enterprise Computing Conference. EDOC 2014 - Ulm, Germany
Duration: 1 Sep 20145 Sep 2014
Conference number: 18


ConferenceThe 18th IEEE Enterprise Computing Conference. EDOC 2014
SeriesInternational Enterprise Distributed Object Computing Conference. Proceedings

ID: 41025594