Computational modeling of social decision-making.

Social decision-making is guided by a complex set of social norms. Computational modeling can play a significant role in enriching our understanding of these norms and how precisely they direct social choices. Here, we highlight three major advantages to using computational modeling, particularly models derived from Utility Theory, in the study of social norms. We illustrate how such models can help generate detailed processes of decision-making, enforce theoretical precision by delineating abstract concepts, and unpack when, and why, people adhere to specific social norms. For each benefit, we discuss a recent study which has employed modeling in the service of assessing the role of norms in decision-making, collectively revealing how computational modeling enables better prediction, description, and explanation of important social choices.

Moral judgment of objectionable online content: Reporting decisions and punishment preferences on social media.

Harmful and inappropriate online content is prevalent, necessitating the need to understand how individuals judge and wish to mitigate the spread of negative content on social media. In an online study with a diverse sample of social media users (n = 294), we sought to elucidate factors that influence individuals' evaluation of objectionable online content. Participants were presented with images varying in moral valence, each accompanied by an indicator of intention from an ostensible content poster. Half of the participants were assigned the role of user content moderator, while the remaining participants were instructed to respond as they normally would online. The study aimed to establish whether moral imagery, the intention of a content poster, and the perceived responsibility of social media users, affect judgments of objectionability, operationalized through both decisions to flag content and preferences to seek punishment of other users. Our findings reveal that moral imagery strongly influences users' assessments of what is appropriate online content, with participants almost exclusively choosing to report and punish morally negative images. Poster intention also plays a significant role in user's decisions, with greater objection shown to morally negative content when it has been shared by another user for the purpose of showing support for it. Bestowing a content moderation role affected reporting behaviour but not punishment preferences. We also explore individual user characteristics, finding a negative association between trust in social media platforms and reporting decisions. Conversely, a positive relationship was identified between trait empathy and reporting rates. Collectively, our insights highlight the complexity of social media users' moderation decisions and preferences. The results advance understanding of moral judgments and punishment preferences online, and offer insights for platforms and regulatory bodies aiming to better understand social media users' role in content moderation.