Identifying Nonprofits by Scaling Mission and Activity with Word Embedding

Abstract

Abstract This study develops a new text-as-data method for organization identification, based on word embedding. We introduce and apply the method to identify identity-based nonprofit organizations, using the U.S. nonprofits’ mission and activity information reported in the IRS Form 990s in 2010–2016. Our results show that such method is simple but versatile. It complements the existing dictionary-based approaches and supervised machine learning methods for classification purposes and generates a reliable continuous measure of document-to-keyword relevance. Our approach provides a nonbinary alternative for nonprofit big data analyses. Using word embedding, researchers are able to identify organizations of interest, track possible changes over time and capture nonprofits’ multi-dimensionality.

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