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Category: Project

Description: Social media users encounter an endless stream of emotional content every time they open their feeds. To make sense of this content and to understand the general emotional sentiment, users need 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 a mock social media feed, we showed participants news articles, followed by sequences of user responses. Participants estimated the emotional intensity of each response as well as the overall average emotionality of the response sequence. We found that participants overestimated the average emotionality of response sequences compared to the actual average based on their individual ratings (Study 1a). Overestimation of response sequences also led to stronger emotional reactions to news (Study 1b). Exploring the mechanism suggested stronger memory for more emotional responses (Study 2). We further provided proof that the emotional intensity of responses was the driver of overestimation by replicating the findings in sequences of individual words (Study 3). We then turned to the consequences of overestimation, showing it was associated 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 of willingness to share articles. This set of findings sheds light on how sampling from newsfeeds amplifies the perception of emotionality.