The COVID-19 pandemic is increasing negative emotions and decreasing positive emotions globally. Left unchecked, these emotional changes may have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we will examine the impact of reappraisal, a widely studied and highly effective form of emotion regulation. Participants from 55 countries (expected N = 25,448) will be randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing), an active control condition, or a passive control condition. We predict that both reappraisal interventions will reduce negative emotions and increase positive emotions relative to the control conditions. We further predict that reconstrual will decrease negative emotions more than repurposing, and that repurposing will increase positive emotions more than reconstrual. We hope to inform efforts to create a scalable intervention for use around the world to build resilience during the pandemic and beyond.

People spend considerable time on digital media, and during this time they are often exposed to others’ emotion expressions. This exposure can lead their own emotion expressions to become more like others’ emotion expressions, a process we refer to as digital emotion contagion. This paper reviews the growing literature on digital emotion contagion. After defining emotion contagion, we suggest that one unique feature of digital emotion contagion is that it is mediated by digital media platforms that are motivated to upregulate users’ emotions. We then turn to measurement, consider the challenges of demonstrating that digital emotion contagion has occurred, and determine how these challenges have been addressed. Finally, we call for a greater focus on understanding when emotion contagion effects will be strong versus weak or nonexistent.

When exposed to others’ emotional responses, people often make rapid decisions as to whether these others are members of their group or not. These group categorization decisions have been shown to be extremely important to understanding group behavior. Yet, despite their prevalence and importance, we know very little about the attributes that shape these categorization decisions. To address this issue, we took inspiration from ensemble coding research and developed a task designed to reveal the influence of the mean and variance of group members’ emotions on participants’ group categorization. In Study 1, we verified that group categorization decreases when the group’s mean emotion is different from the participant’s own emotional response. In Study 2, we established that people identify a group’s mean emotion more accurately when its variance is low rather than high. In Studies 3 and 4, we showed that participants were more likely to self-categorize as members of groups with low emotional variance, even if their own emotions fell outside of the range of group emotions they saw, and that this preference is seen for judgments of both positive and negative group emotions. In Study 5, we showed that this unique preference for low group emotional variance is special to group categorization and does not appear in a more basic face categorization task. Our studies reveal unexplored and important tendencies in group categorization based on group emotions.

When analyzing situations in which multiple people are experiencing emotions together—whether the emotions are positive or negative and whether the situations are online or offline—we are intuitively drawn to the emotions of each individual in the situation. However, this type of analysis often seems incomplete. In many of the cases in which people experience emotions together, there appear to be emergent macro-level affective processes that cannot be readily captured at the individual level. This paper examines these macro-level affective phenomena, termed collective emotions. We open with a general review of research on collective psychological processes. We then define collective emotions and discuss their key features. Next, we focus our attention on the emergent properties of collective emotions and map them using three dimensions: quality, magnitude, and time course. Finally, we discuss pressing open questions and future directions.

It is well established that people often express emotions that are similar to those of other group members. However, people do not always express emotions that are similar to other group members, and the factors that determine when similarity occurs are not yet clear. In the current project, we examined whether certain situations activate specific emotional motives that influence the tendency to show emotional similarity. To test this possibility, we considered emotional responses to political situations that either called for weak (Studies 1 and 3) or strong (Study 2 and 4) negative emotions. Findings revealed that the motivation to feel weak emotions led people to be more influenced by weaker emotions than their own, whereas the motivation to feel strong emotions led people to be more influenced by stronger emotions than their own. Intriguingly, these motivations led people to change their emotions even after discovering that others’ emotions were similar to their initial emotional response. These findings are observed both in a lab task (Studies 1–3) and in real-life online interactions on Twitter (Study 4). Our findings enhance our ability to understand and predict emotional influence processes in different contexts and may therefore help explain how these processes unfold in group behavior.