Protest movements around the world have become increasingly likely to incite counterprotests that adopt an opposing stance. This study examines how a protest and a counterprotest interact with and shape each other as digitally networked connective action. My empirical focus is the so-called Million MAGA March—in which supporters of U.S. President Donald Trump protested the “stealing” of the November 2020 election by his rival, Joe Biden—and a counterprotest that erupted simultaneously. Drawing on a computational mixed-methods approach to examine two corpora of tweets featuring hashtags used by protesters and counterprotesters, respectively, the study identifies three mutually reinforcing dimensions of protest–counterprotest interaction: affective repertoires, discursive strategies, and network structures. It argues that “affective polarization”—or negative partisanship driven by hostility toward an outgroup—offers a useful conceptual means of understanding the significance of affect and collective identity in digital social movements, especially protest–counterprotest interactions. In doing so, the study also addresses concerns that “big data” methods are insensitive to the role of identity and expressive communication in social movements. Finally, the study demonstrates how online and offline political action are mutually constitutive aspects of contemporary contentious politics.

世界各地的抗议运动越来越有可能煽动采取反对立场的反抗议。本研究探讨了抗议和反抗议如何作为数字网络连接行动相互作用并相互塑造。我的经验重点是所谓的百万MAGA 游行——美国总统唐纳德·特朗普的支持者抗议他的竞争对手乔·拜登“窃取”了 2020 年 11 月的大选——以及同时爆发的反抗议。该研究利用计算混合方法方法来检查两个分别具有抗议者和反抗议者使用的主题标签的推文语料库，确定了抗议-反抗议互动的三个相互加强的维度：情感曲目、话语策略, 和网络 结构. 它认为，“情感两极分化”——或由对外部群体的敌意驱动的负面党派偏见——提供了一种有用的概念手段，可以理解数字社会运动中情感和集体身份的重要性，尤其是抗议-反抗议互动。在这样做的过程中，该研究还解决了“大数据”方法对社会运动中身份和表达交流的作用不敏感的担忧。最后，该研究展示了在线和离线政治行动如何成为当代争议政治的相互构成方面。