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.

社交媒体在 2018-2019 年围绕枪支、移民和 LGBTQ 权利等三个问题的社会运动中，使活动人士能够直接与公众沟通，并为运动领袖、参与者、旁观者和反对者提供一个共同构建和竞争叙事的空间。我们创建了一个代码书、注释数据集和计算模型，以检测诊断性（问题识别和归因）、预测性（提出解决方案和策略）和动机性（行动呼吁）的框架策略。我们对每个框架策略进行了深入的非监督语言分析，并揭示了框架与代词和义务性情态动词等语言特征之间的运动间关联性。最后，我们比较了不同议题以及其他社会、文化和互动背景下的框架策略。例如，我们表明诊断性框架在回复中比在原始广播帖子中更常见，而社会运动组织更侧重于预测性和动机性框架，而不是记者和普通公民。