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.