@inproceedings{2b4bf7baf66341519a7c4b2f56242574,
title = "Using machine learning and text mining to classify fuzzy social science phenomenon: The case of social innovation",
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.",
author = "Abdullah G{\"o}k and Nikola Milosevic and Goran Nenadic",
year = "2019",
month = sep,
day = "1",
language = "English",
series = "17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings",
publisher = "International Society for Scientometrics and Informetrics",
pages = "2171--2176",
editor = "Giuseppe Catalano and Cinzia Daraio and Martina Gregori and Moed, {Henk F.} and Giancarlo Ruocco",
booktitle = "17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings",
address = "Belgium",
note = "17th International Conference on Scientometrics and Informetrics, ISSI 2019 ; Conference date: 02-09-2019 Through 05-09-2019",
}