Existing studies of social movement organizations (SMOs) commonly focus only on a small number of well-known SMOs or SMOs that belong to a single social movement industry (SMI). This is partially because current methods for identifying SMOs are labor-intensive. In contrast to these manual approaches, in our article, we use Twitter data pertaining to BlackLivesMatter and Women’s movements and employ crowdsourcing and nested supervised learning methods to identify more than 50K SMOs. Our results reveal that the behavior and influence of SMOs vary significantly, with half having little influence and behaving in similar ways to an average individual. Furthermore, we show that collectively, small SMOs contributed to the sharing of more diverse information. However, on average, large SMOs were significantly more committed to movements and decidedly more successful at recruiting. Finally, we also observe that a large number of SMOs from an extensive set of SMIs, including Occupy Wall Street, participated in solidarity or even as leaders in BlackLivesMatter. In comparison, few SMIs participated in Women’s movement.
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