Predictive Analytics with Big Social Data

    Research output: Contribution to conferencePaperResearchpeer-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 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.
    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.

    Conference

    ConferenceInternational Conference on Computational Social Science
    LocationFinlandia Hall
    CountryFinland
    CityAlto
    Period08/06/201511/06/2015
    Internet address

    Cite this

    Buus Lassen, N., Madsen, R., & Vatrapu, R. (2015). Predictive Analytics with Big Social Data. Paper presented at International Conference on Computational Social Science, Alto, Finland.
    Buus Lassen, Niels ; Madsen, René ; Vatrapu, Ravi. / Predictive Analytics with Big Social Data. Paper presented at International Conference on Computational Social Science, Alto, Finland.3 p.
    @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/",

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    Buus Lassen, N, Madsen, R & Vatrapu, R 2015, 'Predictive Analytics with Big Social Data' Paper presented at, Alto, Finland, 08/06/2015 - 11/06/2015, .

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

    2015. Paper presented at International Conference on Computational Social Science, Alto, Finland.

    Research output: Contribution to conferencePaperResearchpeer-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. Paper presented at International Conference on Computational Social Science, Alto, Finland.