Predicting iPhone Sales from iPhone Tweets

Niels Buus Lassen, Rene Madsen, Ravi Vatrapu

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    Resumé

    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.
    OriginalsprogEngelsk
    TitelProceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014
    RedaktørerManfred Reichert, Stefanie Rinderle-Ma, Georg Grossmann
    Antal sider10
    Udgivelses stedLos Alamitos, CA
    ForlagIEEE
    Publikationsdato2014
    Sider81-90
    ISBN (Trykt)9781479954704
    DOI
    StatusUdgivet - 2014
    BegivenhedThe 18th IEEE Enterprise Computing Conference. EDOC 2014: Utilizing Big Data for the Enterprise of the Future - Ulm, Tyskland
    Varighed: 1 sep. 20145 sep. 2014
    Konferencens nummer: 18
    http://www.edoc2014.org/

    Konference

    KonferenceThe 18th IEEE Enterprise Computing Conference. EDOC 2014
    Nummer18
    LandTyskland
    ByUlm
    Periode01/09/201405/09/2014
    Internetadresse
    NavnInternational Enterprise Distributed Object Computing Conference. Proceedings
    Vol/bind18
    ISSN1541-7719

    Emneord

    • Data science
    • Computational social science
    • Social data analytics
    • Predictive analytics
    • iPhone sales
    • iPhone tweets
    • Twitter

    Citer dette

    Lassen, N. B., Madsen, R., & Vatrapu, R. (2014). Predicting iPhone Sales from iPhone Tweets. I M. Reichert, S. Rinderle-Ma, & G. Grossmann (red.), Proceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014 (s. 81-90). Los Alamitos, CA: IEEE. International Enterprise Distributed Object Computing Conference. Proceedings, Bind. 18 https://doi.org/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. red. / Manfred Reichert ; Stefanie Rinderle-Ma ; Georg Grossmann. Los Alamitos, CA : IEEE, 2014. s. 81-90 (International Enterprise Distributed Object Computing Conference. Proceedings, Bind 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 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.",
    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",
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    doi = "10.1109/EDOC.2014.20",
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    series = "International Enterprise Distributed Object Computing Conference. Proceedings",
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    Lassen, NB, Madsen, R & Vatrapu, R 2014, Predicting iPhone Sales from iPhone Tweets. i M Reichert, S Rinderle-Ma & G Grossmann (red), Proceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014. IEEE, Los Alamitos, CA, International Enterprise Distributed Object Computing Conference. Proceedings, bind 18, s. 81-90, The 18th IEEE Enterprise Computing Conference. EDOC 2014, Ulm, Tyskland, 01/09/2014. https://doi.org/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. red. / Manfred Reichert; Stefanie Rinderle-Ma; Georg Grossmann. Los Alamitos, CA : IEEE, 2014. s. 81-90 (International Enterprise Distributed Object Computing Conference. Proceedings, Bind 18).

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

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    Lassen NB, Madsen R, Vatrapu R. Predicting iPhone Sales from iPhone Tweets. I Reichert M, Rinderle-Ma S, Grossmann G, red., Proceedings. IEEE 18th International Enterprise Distributed Object Computing Conference, EDOC 2014. Los Alamitos, CA: IEEE. 2014. s. 81-90. (International Enterprise Distributed Object Computing Conference. Proceedings, Bind 18). https://doi.org/10.1109/EDOC.2014.20