  Social media enables activists to directly communicate with the public and
provides a space for movement leaders, participants, bystanders, and opponents
to collectively construct and contest narratives. Focusing on Twitter messages
from social movements surrounding three issues in 2018-2019 (guns, immigration,
and LGBTQ rights), we create a codebook, annotated dataset, and computational
models to detect diagnostic (problem identification and attribution),
prognostic (proposed solutions and tactics), and motivational (calls to action)
framing strategies. We conduct an in-depth unsupervised linguistic analysis of
each framing strategy, and uncover cross-movement similarities in associations
between framing and linguistic features such as pronouns and deontic modal
verbs. Finally, we compare framing strategies across issues and other social,
cultural, and interactional contexts. For example, we show that diagnostic
framing is more common in replies than original broadcast posts, and that
social movement organizations focus much more on prognostic and motivational
framing than journalists and ordinary citizens.
