Predictive Analytics with Big Social Data

Niels Buus Lassen, René Madsen, Ravi Vatrapu

    Publikation: KonferencebidragPaperForskningpeer review

    2 Downloads (Pure)

    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 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.
    OriginalsprogEngelsk
    Publikationsdato2015
    Antal sider3
    StatusUdgivet - 2015
    BegivenhedInternational Conference on Computational Social Science - Finlandia Hall, Alto, Finland
    Varighed: 8 jun. 201511 jun. 2015
    http://iccss2015.eu/

    Konference

    KonferenceInternational Conference on Computational Social Science
    LokationFinlandia Hall
    LandFinland
    ByAlto
    Periode08/06/201511/06/2015
    Internetadresse

    Citer dette

    Buus Lassen, N., Madsen, R., & Vatrapu, R. (2015). Predictive Analytics with Big Social Data. Afhandling præsenteret på International Conference on Computational Social Science, Alto, Finland.
    Buus Lassen, Niels ; Madsen, René ; Vatrapu, Ravi. / Predictive Analytics with Big Social Data. Afhandling præsenteret på International Conference on Computational Social Science, Alto, Finland.3 s.
    @conference{662ebafd80484e888d8872c6b38545c2,
    title = "Predictive Analytics with Big Social Data",
    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 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.",
    author = "{Buus Lassen}, Niels and Ren{\'e} Madsen and Ravi Vatrapu",
    year = "2015",
    language = "English",
    note = "null ; Conference date: 08-06-2015 Through 11-06-2015",
    url = "http://iccss2015.eu/",

    }

    Buus Lassen, N, Madsen, R & Vatrapu, R 2015, 'Predictive Analytics with Big Social Data' Paper fremlagt ved International Conference on Computational Social Science, Alto, Finland, 08/06/2015 - 11/06/2015, .

    Predictive Analytics with Big Social Data. / Buus Lassen, Niels; Madsen, René; Vatrapu, Ravi.

    2015. Afhandling præsenteret på International Conference on Computational Social Science, Alto, Finland.

    Publikation: KonferencebidragPaperForskningpeer review

    TY - CONF

    T1 - Predictive Analytics with Big Social Data

    AU - Buus Lassen, Niels

    AU - Madsen, René

    AU - Vatrapu, Ravi

    PY - 2015

    Y1 - 2015

    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 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.

    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 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.

    M3 - Paper

    ER -

    Buus Lassen N, Madsen R, Vatrapu R. Predictive Analytics with Big Social Data. 2015. Afhandling præsenteret på International Conference on Computational Social Science, Alto, Finland.