Predicting iPhone Sales from iPhone Tweets

Niels Buus Lassen, Rene Madsen, Ravi Vatrapu

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    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.
    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.
    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
    PublisherIEEE
    Date2014
    Pages81-90
    ISBN (Print)9781479954704
    DOIs
    StatePublished - 2014
    EventThe 18th IEEE Enterprise Computing Conference. EDOC 2014: Utilizing Big Data for the Enterprise of the Future - Ulm, Germany
    Duration: 1 Sep 20145 Sep 2014
    Conference number: 18
    http://www.edoc2014.org/

    Conference

    ConferenceThe 18th IEEE Enterprise Computing Conference. EDOC 2014
    Number18
    CountryGermany
    CityUlm
    Period01/09/201405/09/2014
    Internet address
    SeriesInternational Enterprise Distributed Object Computing Conference. Proceedings
    Volume18
    ISSN1541-7719

    Keywords

      Cite this

      Lassen, N. B., Madsen, R., & Vatrapu, R. (2014). Predicting iPhone Sales from iPhone Tweets. In M. Reichert, S. Rinderle-Ma, & G. Grossmann (Eds.), Proceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014 (pp. 81-90). Los Alamitos, CA: IEEE. International Enterprise Distributed Object Computing Conference. Proceedings, Vol.. 18, DOI: 10.1109/EDOC.2014.20
      Lassen, Niels Buus ; Madsen, Rene ; Vatrapu, Ravi. / Predicting iPhone Sales from iPhone Tweets. Proceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014. editor / Manfred Reichert ; Stefanie Rinderle-Ma ; Georg Grossmann. Los Alamitos, CA : IEEE, 2014. pp. 81-90 (International Enterprise Distributed Object Computing Conference. Proceedings, ???volume??? 18).
      @inproceedings{f5ece677f14040348a23701969a57671,
      title = "Predicting iPhone Sales from iPhone Tweets",
      abstract = "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 foruser/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.",
      keywords = "Data science, Computational social science, Social data analytics, Predictive analytics, iPhone sales, iPhone tweets, Twitter",
      author = "Lassen, {Niels Buus} and Rene Madsen and Ravi Vatrapu",
      year = "2014",
      doi = "10.1109/EDOC.2014.20",
      language = "English",
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      pages = "81--90",
      editor = "Manfred Reichert and Stefanie Rinderle-Ma and Georg Grossmann",
      booktitle = "Proceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014",
      publisher = "IEEE",
      address = "United States",

      }

      Lassen, NB, Madsen, R & Vatrapu, R 2014, Predicting iPhone Sales from iPhone Tweets. in M Reichert, S Rinderle-Ma & G Grossmann (eds), Proceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014. IEEE, Los Alamitos, CA, International Enterprise Distributed Object Computing Conference. Proceedings, vol. 18, pp. 81-90, Ulm, Germany, 01/09/2014. DOI: 10.1109/EDOC.2014.20

      Predicting iPhone Sales from iPhone Tweets. / Lassen, Niels Buus; Madsen, Rene; Vatrapu, Ravi.

      Proceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014. ed. / Manfred Reichert; Stefanie Rinderle-Ma; Georg Grossmann. Los Alamitos, CA : IEEE, 2014. p. 81-90.

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

      TY - GEN

      T1 - Predicting iPhone Sales from iPhone Tweets

      AU - Lassen,Niels Buus

      AU - Madsen,Rene

      AU - Vatrapu,Ravi

      PY - 2014

      Y1 - 2014

      N2 - 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 foruser/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.

      AB - 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 foruser/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.

      KW - Data science

      KW - Computational social science

      KW - Social data analytics

      KW - Predictive analytics

      KW - iPhone sales

      KW - iPhone tweets

      KW - Twitter

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      PB - IEEE

      CY - Los Alamitos, CA

      ER -

      Lassen NB, Madsen R, Vatrapu R. Predicting iPhone Sales from iPhone Tweets. In Reichert M, Rinderle-Ma S, Grossmann G, editors, Proceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014. Los Alamitos, CA: IEEE. 2014. p. 81-90. (International Enterprise Distributed Object Computing Conference. Proceedings, Vol. 18). Available from, DOI: 10.1109/EDOC.2014.20