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

Abdullah Gök, Nikola Milosevic, Goran Nenadic

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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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.

Original languageEnglish
Title of host publication17th International Conference on Scientometrics and Informetrics, ISSI 2019
Subtitle of host publicationProceedings : Volume II
EditorsGiuseppe Catalano, Cinzia Daraio, Martina Gregori, Henk F. Moed, Giancarlo Ruocco
Place of PublicationItaly
Pages2171-2176
Number of pages6
Publication statusPublished - 31 Aug 2019
Event17th International Conference on Scientometrics and Informetrics - Rome, Italy
Duration: 2 Sep 20195 Sep 2019

Conference

Conference17th International Conference on Scientometrics and Informetrics
Abbreviated titleISSI 2019
Country/TerritoryItaly
CityRome
Period2/09/195/09/19

Keywords

  • machine learning
  • text mining
  • social Innovation
  • scientometrics
  • informetrics

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