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.
Social distancing is currently the single most effective method to reduce the spread of COVID-19. As such, researchers across varying fields are rushing to identify variables that predict social distancing and which interventions can heighten social distancing. Yet, much of this research relies on self-report measures (in part because of social distancing guidelines themselves). In two studies we examine whether self-reported social distancing overlaps with real-world behavior. In Study 1, individuals’ self-reported social distancing predicted decreased movement as quantified by participants’ average daily step-counts (assessed via smartphone pedometers). For every increase of one in self-reported social distancing (z-scored), individuals’ daily steps decreased by approximately 21% (Exp(B) ~ .79). In Study 2, the degree of self-reported social distancing in different U.S. States predicted the degree to which people in those States reduced their overall movement and travel to non-essential retail as assessed by ~17 million smart-phone GPS coordinates (.34 < rs < .57). Collectively, our results indicate that self-report measures of social distancing track actual behavior both at the individual and at the group level.
In an effort to combat COVID-19 and future pandemics, researchers have attempted to identify the factors underlying social distancing. Yet, much of this research relies on self-report measures. In two studies, we examine whether self-reported social distancing predicts objective distancing behavior. In Study 1, individuals’ self-reported social distancing predicted decreased mobility (assessed via smartphone step counts) during the COVID-19 pandemic. While participants high in self-reported distancing (+1 SD) exhibited a 33% reduction in daily step counts, those low in distancing (−1 SD) exhibited only a 3% reduction. Study 2 extended these findings to the group level. Self-reported social distancing at the U.S. state level accounted for 20% of the variance in states’ objective reduction in overall movement and visiting nonessential services (calculated via the GPS coordinates of ∼15 million people). Collectively, our results indicate that self-reported social distancing tracks actual social distancing behavior.
Judgments of whether an action is morally wrong depend on who is involved and the nature of their relationship. But how, when, and why social relationships shape moral judgments is not well understood. We provide evidence to address these questions, measuring cooperative expectations and moral wrongness judgments in the context of common social relationships such as romantic partners, housemates, and siblings. In a pre-registered study of 423 U.S. participants nationally representative for age, race, and gender, we show that people normatively expect different relationships to serve cooperative functions of care, hierarchy, reciprocity, and mating to varying degrees. In a second pre-registered study of 1,320 U.S. participants, these relationship-specific cooperative expectations (i.e., relational norms) enable highly precise out-of-sample predictions about the perceived moral wrongness of actions in the context of particular relationships. In this work, we show that this ‘relational norms’ model better predicts patterns of moral wrongness judgments across relationships than alternative models based on genetic relatedness, social closeness, or interdependence, demonstrating how the perceived morality of actions depends not only on the actions themselves, but also on the relational context in which those actions occur.
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.
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