How social learning amplifies moral outrage expression in online social networks

Abstract

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 (N = 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.

Fig. 1. Distributions of ideological extremity of user networks and levels of outrage expression.

(A) Density plots of the ideological extremity of user networks for the Kavanaugh dataset (study 1) and United dataset (study 2). The x axis represents a continuous estimate of the mean ideological extremity of a user’s network; greater values represent greater extremity. (B) Each user’s median probability of expressing outrage in their tweets as a function of the ideological extremity of their network.

Fig. 2. Network-level ideological extremity moderates the effect of social feedback on outrage expressions.

Each point displays the effect size estimate of previous social feedback, predicting current outrage expression. Error bars were calculated on the basis of SEs of the estimate. The x axis represents quantile breaks from 20 to 80%. The blue color represents the Kavanaugh dataset users (study 1), and the orange color represents the United dataset users (study 2).

Fig. 3. Depiction of social media learning task (studies 3 and 4).

Participants first viewed what types of expressions were normative in their network by scrolling through 12 tweets. Next, they participated in a learning task where their goal was to maximize feedback.

Fig. 4. Reinforcement learning and norm learning shape outrage expressions in a simulated social media environment.

The y axis represents the percentage of participants (Ps) on each trial that selected outrage tweets to post. The x axis represents the trial number. The red line represents participants in the outrage norm condition while the grey line represents participants in the neutral norm condition. Error bands represent the standard errors produced by fitting with a GAM function in R 3.6.1. The dotted line represents a 50% selection rate for participants in a given trial. Panel (A) displays results for Study 1, Panel (B) displays results for Study 2.