Using machine learning and text mining to classify fuzzy social science phenomenon : the case of social innovation

Gök, Abdullah and Milosevic, Nikola and Nenadic, Goran; Catalano, Giuseppe and Daraio, Cinzia and Gregori, Martina and Moed, Henk F. and Ruocco, Giancarlo, eds. (2019) Using machine learning and text mining to classify fuzzy social science phenomenon : the case of social innovation. In: 17th International Conference on Scientometrics and Informetrics, ISSI 2019. International Society for Scientometrics and Informetrics, ITA, pp. 2171-2176. ISBN 9788833811185

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    Abstract

    Classifying social science concepts by using machine learning and text-mining is often very challenging, particularly due to the fact that social concepts are often defined in a vague manner. In this paper, we put forward a first conceptual step to overcome this challenge. By using the case of social innovation, which has 252 distinct definitions, we qualitatively demonstrated that these definitions group around four different themes where various definitions utilise one or multiple of these criteria in different combinations to define social innovations. We designed an experiment where a database of social innovation projects annotated i) based on an overall understanding and ii) based on a decomposed definition of four criteria. As a next step, we will test the performance of various model specification on these two approaches.

    ORCID iDs

    Gök, Abdullah ORCID logoORCID: https://orcid.org/0000-0002-9378-3336, Milosevic, Nikola and Nenadic, Goran; Catalano, Giuseppe, Daraio, Cinzia, Gregori, Martina, Moed, Henk F. and Ruocco, Giancarlo