Methods for Classifying Nonprofit Organizations According to their Field of Activity: A Report on Semi-automated Methods Based on Text

There are various methods for classifying nonprofit organizations (NPOs) according to their field of activity. We report our experiences using two semi-automated methods based on textual data: rule-based classification and machine learning with curated keywords. We use those methods to classify Austrian nonprofit organizations based on the International Classification of Nonprofit Organizations. Those methods can provide a solution to the widespread research problem that quantitative data on the activities of NPOs are needed but not readily available from administrative data, long high-quality texts describing NPOs' activities are mostly unavailable, and human labor resources are limited. We find that in such a setting, rule-based classification performs about as well as manual human coding in terms of precision and sensitivity, while being much more labor-saving. Hence, we share our insights on how to efficiently implement such a rule-based approach. To address scholars with a background in data analytics as well as those without, we provide non-technical explanations and open-source sample code that is free to use and adapt

Methods for Classifying Nonprofit Organizations According to their Field of Activity: A Report on Semiautomated Methods Based on Text

In this research note we discuss the two basic computational methods available for categorizing nonprofit organizations (NPOs) according to their field of activity based on textual information about these organizations: (1) rule-based categorization and (2) pattern recognition by using machine learning techniques. These methods provide a solution to the widespread research problem that quantitative data on the activities of NPOs are needed but not readily available from administrative data, and that manual categorization is not feasible for large samples. We explain both methods and report our experience in using them to categorize Austrian nonprofit associations on the basis of the International Classification of Non-Profit Organizations (ICNPO). Since we have found that rule-based categorization works much better for this task than machine learning, we provide detailed recommendations for implementing a rule-based approach. We address scholars with a background in data analytics as well as those without, by providing non-technical explanations as well as open-source sample code that is free to use and adapt

Civil Society in Central and Eastern Europe: Monitoring 2019

Presently, the culture of open discussion seems to be threatened in an increasing number of countries. In Central and Eastern Europe's (CEE's) democracies, recent political developments appear to jeopardize progresses made in the past. Against this background, this study aims at shedding light on the dynamics of CEE'scivil society and gives a brief overview of the status quo and recent developments that directly affect civil society. The study was conducted by the Competence Center for Nonprofit Organizations and Social Entrepreneurship at WU Vienna (Vienna University of Economics and Business), commissioned by and in collaboration with ERSTE foundation as well as with a group of country experts. The inclusion of expert assessments on civil society aims at giving a voice primarily to practitioners. Therefore, the study included an online survey in each participating country, addressing CSO representatives operating in various fields of activity