Sequential Sampling from Social Media Feeds leads to Overestimation of Emotional Intensity

Social media users encounter an endless stream of emotional content every time they open theirfeeds. To make sense of this content and to understand the general emotional sentiment, usersneed to aggregate the influx of emotional expressions into representations of what others feel.How do users generate these aggregated evaluations? Across six studies (N = 1,051), using amock social media feed, we showed participants news articles, followed by sequences of userresponses. Participants estimated the emotional intensity of each response as well as the overallaverage emotionality of the response sequence. We found that participants overestimated theaverage emotionality of response sequences compared to the actual average based on theirindividual ratings (Study 1a). Overestimation of response sequences also led to strongeremotional reactions to news (Study 1b). Exploring the mechanism suggested stronger memoryfor more emotional responses (Study 2). We further provided proof that the emotional intensity ofresponses was the driver of overestimation by replicating the findings in sequences of individualwords (Study 3). We then turned to the consequences of overestimation, showing it wasassociated with perceiving more intense emotional responses as more representative of the norm(Study 4), and with overestimation of the emotionality of the newsfeed as a whole (Study 5).Estimation of the average emotionality of the response sequence was also predictive ofwillingness to share articles. This set of findings sheds light on how sampling from newsfeedsamplifies the perception of emotionality.