Brady, W. J., McLoughlin, K. L., et al.
(2021, January 19).
https://doi.org/10.31234/osf.io/gf7t5
Abstract
Moral outrage shapes fundamental aspects of human 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 pre-registered observational studies of Twitter (7,331 users and 12.7 million total tweets) and two pre-registered 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. We also find that outrage expressions are sensitive to expressive norms in users’ social networks, over and above users’ own preferences, suggesting that norm learning processes guide online outrage expressions. Moreover, expressive norms moderate social reinforcement of outrage: 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 impact moral discourse in digital public spaces.
From the Conclusion
At first blush, documenting the role of reinforcement learning in online outrage expressions may seem trivial. Of course, we should expect that a fundamental principle of human behavior, extensively observed in offline settings, will similarly describe behavior in online settings. However, reinforcement learning of moral behaviors online, combined with the design of social media platforms, may have especially important social implications. Social media newsfeed algorithms can directly impact how much social feedback a given post receives by determining how many other users are exposed to that post. Because we show here that social feedback impacts users’ outrage expressions over time, this suggests newsfeed algorithms can influence users’ moral behaviors by exploiting their natural tendencies for reinforcement learning. In this way, reinforcement learning on social media differs from reinforcement learning in other environments because crucial inputs to the learning process are shaped by corporate interests. Even if platform designers do not intend to amplify moral outrage, design choices aimed at satisfying other goals --such as profit maximization via user engagement --can indirectly impact moral behavior because outrage-provoking content draws high engagement. Given that moral outrage plays a critical role in collective action and social change, our data suggest that platform designers have the ability to influence the success or failure of social and political movements, as well as informational campaigns designed to influence users’ moral and political attitudes. Future research is required to understand whether users are aware of this, and whether making such knowledge salient can impact their online behavior.
People are more likely to express online "moral outrage" if they have either been rewarded for it in the past or it's common in their own social network. They are even willing to express far more moral outrage than they genuinely feel in order to fit in.
