Department of Psychology, University of Bath.
School of Management, University of Bath.
J Pers Soc Psychol. 2023 Sep;125(3):496-518. doi: 10.1037/pspa0000338. Epub 2023 Feb 13.
Since 2009, there has been an increase in global protests and related online activity. Yet, it is unclear how and why online activity is related to the mobilization of offline collective action. One proposition is that online polarization (or a relative change in intensity of posting mobilizing content around a salient grievance) can mobilize people offline. The identity-norm nexus and normative alignment models of collective action further argue that to be mobilizing, these posts need to be socially validated. To test these propositions, across two analyses, we used digital traces of online behavior and data science techniques to model people's online and offline behavior around a mass protest. In Study 1a, we used Twitter behavior posted on the day of the protest by attendees or nonattendees (759 users; 7,592 tweets) to train and test a classifier that predicted, with 80% accuracy, who participated in offline collective action. Attendees used their mobile devices to plan logistics and broadcast their presence at the protest. In Study 1b, using the longitudinal Twitter data and metadata of a subset of users from Study 1a (209 users; 277,556 tweets), we found that participation in the protest was not associated with an individual's online polarization over the year prior to the protest, but it was positively associated with the validation ("likes") they received on their relevant posts. These two studies demonstrate that rather than being low cost or trivial, socially validated online interactions about a grievance are actually key to the mobilization and enactment of collective action. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
自 2009 年以来，全球抗议活动和相关的在线活动有所增加。然而，目前尚不清楚在线活动如何以及为何与线下集体行动的动员有关。有一种观点认为，线上极化（或围绕突出不满的动员内容的相对强度变化）可以动员人们离线行动。集体行动的身份-规范关系和规范调整模型进一步认为，要具有动员性，这些帖子需要得到社会认可。为了检验这些假设，我们在两项分析中使用了在线行为的数字痕迹和数据科学技术，来模拟人们在大规模抗议活动前后的线上和线下行为。在研究 1a 中，我们使用了参加者或非参加者在抗议日发布的 Twitter 行为（759 名用户；7592 条推文）来训练和测试一个分类器，该分类器以 80%的准确率预测了谁参加了线下集体行动。参加者使用移动设备来规划后勤工作，并在抗议活动中传播自己的存在。在研究 1b 中，我们使用纵向 Twitter 数据和研究 1a 中部分用户的元数据（209 名用户；277556 条推文），发现参与抗议活动与个人在抗议前一年的线上极化程度无关，但与他们在相关帖子上收到的认可（“点赞”）呈正相关。这两项研究表明，引起共鸣的线上互动并不是成本低廉或微不足道的，而是实际上是动员和实施集体行动的关键。
