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Building the European Social Innovation Database with Natural Language Processing and Machine Learning.

Abdullah Gök1, Roseline Antai2, Nikola Milošević3,4

  • 1Strathclyde Business School, University of Strathclyde, 199 Cathedral Street, Glasgow, G4 0QU, United Kingdom. abdullah.gok@strath.ac.uk.

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The European Social Innovation Database (ESID) addresses the data gap in social innovation research by collecting and classifying global projects. Advanced machine learning models extract key features, making social innovation data accessible for research.

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Area of Science:

  • Social Sciences
  • Computer Science
  • Data Science

Background:

  • Social innovation, defined as new products, services, or models meeting social needs and fostering collaboration, lacks comprehensive data for research.
  • Existing data limitations hinder a thorough understanding and analysis of social innovation initiatives globally.

Purpose of the Study:

  • To address the data gap in social innovation research.
  • To create a comprehensive and accessible database of social innovation projects worldwide.
  • To leverage advanced computational methods for data collection and characterization.

Main Methods:

  • Developed the European Social Innovation Database (ESID) through large-scale collection of unstructured website text.
  • Employed advanced machine learning techniques to extract features like social innovation dimensions, project locations, summaries, and topics.
  • Achieved high model performance with an F1 score of up to 0.90.

Main Results:

  • ESID currently contains 11,468 social innovation projects from 159 countries.
  • The database includes extracted features such as project summaries, locations, and thematic classifications.
  • Data is freely available and accessible via a web-based application.

Conclusions:

  • ESID provides a valuable, data-driven resource for social science research on social innovation.
  • The database facilitates a deeper understanding of global social innovation trends and impacts.
  • Future plans include database expansion, variable addition, and regular data updates.