Brady William J, McLoughlin Killian, Doan Tuan N, Crockett Molly J
Department of Psychology, Yale University, New Haven, CT, USA.
Department of Statistics and Data Science, Yale University, New Haven, CT, USA.
Sci Adv. 2021 Aug 13;7(33). doi: 10.1126/sciadv.abe5641. Print 2021 Aug.
Moral outrage shapes fundamental aspects of social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two preregistered observational studies on Twitter (7331 users and 12.7 million total tweets) and two preregistered behavioral experiments ( = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. In addition, users conform their outrage expressions to the expressive norms of their social networks, suggesting norm learning also guides online outrage expressions. Norm learning overshadows reinforcement learning when normative information is readily observable: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to affect moral discourse in digital public spaces.
道德义愤塑造了社会生活的基本方面,如今在在线社交网络中广泛存在。在此,我们展示了社会学习过程如何随着时间的推移放大在线道德义愤的表达。在两项针对推特的预注册观察性研究(7331名用户和总计1270万条推文)以及两项预注册行为实验(N = 240)中,我们发现,对义愤表达的积极社会反馈会增加未来义愤表达的可能性,这与强化学习的原则一致。此外,用户会使他们的义愤表达符合其社交网络的表达规范,这表明规范学习也指导着在线义愤表达。当规范信息易于观察时,规范学习会掩盖强化学习:在意识形态极端的网络中,义愤表达更为常见,用户在决定是否表达义愤时对社会反馈不太敏感。我们的研究结果凸显了平台设计如何与人类学习机制相互作用,以影响数字公共空间中的道德话语。