Human-algorithm interactions help explain the spread of misinformation.

Human attention biases toward moral and emotional information are as prevalent online as they are offline. When these biases interact with content algorithms that curate social media users' news feeds to maximize attentional capture, moral and emotional information are privileged in the online information ecosystem. We review evidence for these human-algorithm interactions and argue that misinformation exploits this process to spread online. This framework suggests that interventions aimed at combating misinformation require a dual-pronged approach that combines person-centered and design-centered interventions to be most effective. We suggest several avenues for research in the psychological study of misinformation sharing under a framework of human-algorithm interaction.

Overperception of moral outrage in online social networks inflates beliefs about intergroup hostility.

As individuals and political leaders increasingly interact in online social networks, it is important to understand the dynamics of emotion perception online. Here, we propose that social media users overperceive levels of moral outrage felt by individuals and groups, inflating beliefs about intergroup hostility. Using a Twitter field survey, we measured authors' moral outrage in real time and compared authors' reports to observers' judgements of the authors' moral outrage. We find that observers systematically overperceive moral outrage in authors, inferring more intense moral outrage experiences from messages than the authors of those messages actually reported. This effect was stronger in participants who spent more time on social media to learn about politics. Preregistered confirmatory behavioural experiments found that overperception of individuals' moral outrage causes overperception of collective moral outrage and inflates beliefs about hostile communication norms, group affective polarization and ideological extremity. Together, these results highlight how individual-level overperceptions of online moral outrage produce collective overperceptions that have the potential to warp our social knowledge of moral and political attitudes.

How social relationships shape moral wrongness judgments.

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.