3 research outputs found
Predictive Analytics with Big Social Data
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 and facebook can be used to predict the quarterly sales of iPhones and revenues of H&M respectively. 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 linear regression models that transform (a) iPhone tweets into a prediction of the quarterly iPhone sales with an average error close to the established prediction models from investment banks (Lassen, Madsen, & Vatrapu, 2014)and (b) facebook likes into a prediction of the global revenue of the fast fashion company, H&M. We discuss the findings and conclude with implications for predictive analytics with big social data
Predicting iPhone Sales from iPhone Tweets
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
New Fundamental Technologies in Data Mining
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining